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copy 2019 Fraunhofer IWES 1copy 2019 Fraunhofer IWES 1copy 2019 Fraunhofer IWES 1
G Wolken-Moumlhlmann J Gottschall
Dependence of Floating LiDAR Performance on External
Parameters ndash Are existing onshore classification
methods Applicable
EERA Deepwind 2020 Trondheim 15-17 Jan 2020
2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES
Introduction
FLS verification vs class ification
Case Study Fraunhofer IWES LiDAR Buoy
Resume
Outline
3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES
Introduction
Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or
measurements number growing FLS can replace offshore
meteorological masts for site assessment power curve measurements etchellip
From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]
4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES
Introduction
FLS verification vs class ification
Case Study Fraunhofer IWES LiDAR Buoy
Resume
Outline
3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES
Introduction
Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or
measurements number growing FLS can replace offshore
meteorological masts for site assessment power curve measurements etchellip
From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]
4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES
Introduction
Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or
measurements number growing FLS can replace offshore
meteorological masts for site assessment power curve measurements etchellip
From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]
4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES
Introduction
Technology Applications
Wind resource assessment (WRA)
Power curve measurements
hellip
Standards Recommended
practices
VerificationClass ification
[5]
[234]
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES
FLS verification vs classificationVerification Class ification
For a distinct system For selected conditions short term measurement ~1 month
For a FLS type Correlation WSP deviation and
independent variable At least 3 months measurement
hellip
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES
Fraunhofer IWES LiDAR Buoy
System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator
batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t
Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES
Verification
Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1)
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES
Classification ndash Environmental Variables
Wind speed deviation (FLS -Reference) vs environmental variables (EV)
Wind speed Wind direction Wind shear Wind veer Temperature and
temperature difference Air density hellip
Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables
Wave height Wave period Water level Currents hellip
Tilting Yawing Heave Translation hellip
Platform motion variables
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear (example)
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear
FLS is sensitive for independent variable if
- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height Hs
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES
Classification - Sensitivity
Wind shear Significant Wave height HsSensitive Not sensitive
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
Are the variables independent correlations
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
LiDAR Quality Parameter and meteorological variables (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR
2 Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
CNR signal quality 590 -011 -065 001 -006 yes
Wind shear exponent 011 -918 -097 012 -033 yes
Wind veer 013 -966 -121 004 -023 yes
Wind speed 316 -020 -062 006 -015 yes
Turbulence intensity Ti 227 036 081 005 018 yes
Temperature gradient 001 -10426 -110 001 -013 yes
- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable
See Barker Et al [6]
Considering
shear
no
no
yes
no
no
no
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES
Classification ndash Variable Sensitivity Results
Oceanographic variables and motion variable (selection)
Independant variable
std(Indepe
ndant
variable)
m (bin
Fit)
Sensitivity
m x stdR2
Sensitivity
x RSensitive
[-][unit
variable]
[ unit
variable][] [-] []
Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no
Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no
Current 0096 -1382 -0133 0002 -0006 no
Heave range 0570 -0219 -0125 0000 -0002 no
Tilt Range 3811 0027 0105 0000 0001 no
Yaw increment range 8559 -0008 -0069 0002 -0003 no
Static tilt 0473 -0387 -0183 0002 -0008 no
- No sensitivities for oceanographic or platform motion variables
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES
Classification ndash Final classification
Classification results for FINO1 campaign
-gt Most uncertainty comes from reference measurement uncertainty
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES
Classification ndash Results
FLS (Windcube) FLS (ZXZephIR) 100m
For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified
Independant variableSensitivity
m x std
Sensitivit
y x RSensitive
[-] [] []
Significant wave height (buoy) -0101 -0001 no
Peak period Tp (Buoy) 0059 0001 no
Current -0133 -0006 no
Heave range -0125 -0002 no
Tilt Range 0105 0001 no
Yaw increment range -0069 -0003 no
Static tilt -0183 -0008 no
Independant variableSensitivity
m x std
Sensitivity
x rSensitive
[-] [] []
Significant wave height -0063 -0001 no
Peak period Tp (Buoy) -0191 -0007 no
Tm02 (radar) 0013 0000 no
Waterlevel -0069 0000 no
Heave range -0118 -0002 no
Tilt Range 0078 0000 no
Yaw increment range -0054 -0001 no
Static tilt 0075 0002 no
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES
Classification ndash Shortcomings
Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor
design changes
Meteorological variables
FLS set-up
Oceanographic variables
Motion variables
Measurement
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES
Resume
Verification and classification are important for the commercial acceptance of FLS
Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables
Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES
Federal Republic of Germany
Federal Ministry for Economic Affairs and Energy
Federal Ministry of Education and Research
European Regional Development Fund (ERDF)
Federal State of Bremen
Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH
Federal State of Lower Saxony
Free and Hanseatic City of Hamburg
Acknowledgements
Fraunhofer IWES is funded by
The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES
Thanks a lot for your attention
gerritwolken-moehlmanniwesfraunhoferde
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES
References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain
top related