1
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Tension Leg Spar-type Offshore Wind Turbine with Upwind or
Downwind Rotor Configuration
Madjid KarimiradTorgeir Moan
www.cesos.ntnu.no NTNU – Norwegian University of Science and Technology
2
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Outline
Introduction• Floating Wind Turbines
• Spar-type wind turbine• Tension leg spar (TLS)
ChallengesModeling
Theory
Case studies
Remarks and conclusions
www.cesos.ntnu.no Madjid Karimirad -May-2011
3
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Wind industry moved to Offshore
Onshore: • visual and noise impacts• best sites are already in use
Offshore:• cannot be seen or heard• offshore wind is steadier and stronger produce more electricity
Good-potential in moderate and deep water: USA, UK, Norway and Japan floating wind turbines
• Cost-effective solution:(cost of fixed turbines increases with water depth)(practical issues such as installation and design are affected by depth)
• Conflicts with tourism, military and naval forces, sailing and shipping are less
Similar to what experienced in the offshore oil and gas technology.
www.cesos.ntnu.no Madjid Karimirad -May-2011
4
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Floating wind turbines concepts Spar, TLP, Semi-submersible
www.cesos.ntnu.no Madjid Karimirad -May-2011
5
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Tension Leg Spar (TLS) similar to SWAY
Downwind/upwind
www.cesos.ntnu.no Madjid Karimirad -May-2011
6
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
HAWC2, USFOSModeling and theory
Hydrodynamics DLL interface for mooring system action,
Full modeling of mooring systems
Structure
AerodynamicsControl
Aero-hydro-servo-elastic
www.cesos.ntnu.no Madjid Karimirad -May-2011
7
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Hydrodynamic code-to-code comparisonHAWC2 and USFOS/vpOne
• Morison formula, consideration of instantaneous position of structure, using pressure integration method for heave;fully nonlinear
• USFOS: Dynamic pressure integration (transversal) instead of Froude-Krylov terms
www.cesos.ntnu.no Madjid Karimirad -May-2011
8
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Hydrodynamic code-to-code comparison (cont.) HAWC2 and USFOS/vpOne
Tension leg introduces nonlinear effects on the spar motion (The difference and sum frequency responses).
www.cesos.ntnu.no Madjid Karimirad -May-2011
9
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Hydrodynamic code-to-code comparison (cont.) HAWC2 and USFOS/vpOne
www.cesos.ntnu.no Madjid Karimirad -May-2011
10
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Hydrodynamic code-to-code comparison (cont.) HAWC2 and USFOS/vpOne
Tension leg introduces nonlinear effects on the spar motion (The difference and sum frequency responses).
www.cesos.ntnu.no Madjid Karimirad -May-2011
11
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Wave-induced vs. wave-wind-induced
www.cesos.ntnu.no Madjid Karimirad -May-2011
12
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Wave-induced vs. wave-wind-induced
mean of the responses are wind-induced and the standard deviations of the responses are primarily wave-induced
www.cesos.ntnu.no Madjid Karimirad -May-2011
13
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Rotor configuration upwind vs. downwind
www.cesos.ntnu.no Madjid Karimirad -May-2011
14
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Rotor configuration upwind vs. downwind
www.cesos.ntnu.no Madjid Karimirad -May-2011
15
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Rotor configuration (cont.) upwind vs. downwind
www.cesos.ntnu.no Madjid Karimirad -May-2011
16
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Power
The rotor configuration (tower shadow) have limited effects on the power.
Turbulence has some influence on the quality of power production (STD).
0
1
2
3
4
5
6
6 8 10 12 14 16 18
Mean wind speed (m/sec)
Po
wer
(MW
)
CMS-offshoreNREL-onshoreTLS-offshore
Advanced controlmay help!?
www.cesos.ntnu.no Madjid Karimirad -May-2011
17
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Conclusions
TLS, resonant responses, are affected by nonlinear (motion-induced) forces through tension.
The nonlinear effect of the tension leg introduces the sum and difference of the pitch, surge and wave frequencies (Combined-frequencies)
Wave frequency responses are not very much affected by wind loading in cases with combined wave and wind loads
The pitch resonant response is damped for below-rated wind case due to aerodynamic damping
The surge resonant response excited by aero-servo-induced actions is dominant in the rated wind speed case
For operational below- and over-rated wind speed cases the responses are governed by wave-induced responses
For survival cases the aerodynamic damping helps reducing the pitch motion resonance. In storm and hurricane conditions the wave loading is dominant compared to wind loads
www.cesos.ntnu.no Madjid Karimirad -May-2011
18
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structures
Conclusions (cont.)
The responses and electrical power are very close for these two rotor configurations.
However, some small differences can be seen which can be due to the minor modification applied on the upwind rotor to make a downwind.
However for land based wind turbines the extreme response occurs usually in operational cases related to rated wind speed but for floating wind turbines the extreme responses can occur in harsh environmental conditions when the rotor is parked.
Further research
In the present research the collective blade pitch control is applied. More advanced controlalgorithms such as individual blade pitch controller for tension leg spar wind turbine conceptis needed to stabilize the yaw motion.
The present paper is limited to constant wind cases. Studying the turbulence effect on the dynamic response of the TLS wind turbine subjected to stochastic wind and wave loading should be performed. Moreover, the relative magnitude of tower shadow effects compared to turbulence can be investigated.
www.cesos.ntnu.no Madjid Karimirad -May-2011
19
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structureswww.cesos.ntnu.no Madjid Karimirad -May-2011
Thanks for your attention
Manjil (100 MW), Iran
20
www.cesos.ntnu.no Author – Centre for Ships and Ocean Structureswww.cesos.ntnu.no Madjid Karimirad -May-2011
Questions and Answers
On Estimation of Hurricane Risks to Offshore Wind Farms
Hamidreza Arabshahi1 & Lance Manuel2
1PhD Student2Professor and Fluor Centennial Teaching Fellow in Engineering
Dept. of Civil, Architectural, and Environmental EngineeringThe University of Texas at Austin
Scientific Track: Session 8E Delivering Competitive Energy Costs from Offshore Wind Power
Wednesday, May 25, 2011
Outline
Overview of the project
Simulation of hurricane tracks
Verification of track simulation
Simulation of hurricane wind field
Evaluation of turbine loads during a hurricane
Concluding remarks
22
Summary of Steps Involved
Wind farm siteselection
Collection of historical hurricane data
Stochastic simulation of hurricanes
Selection of tracks thatpass through or near the
chosen wind farm
Model for evolutionof hurricane characteristic
parameters
Simulation of wind field at location of interest
along the track
Analysis of wind turbines based on constructed
wind field
Estimation of wind speed exceedance probabilities
at turbine hub height
LossEstimation
(1) (2) (3)
(5)
(6)(7)
(8)
(9)
23
(4)
Candidate Wind Farm Site in the Gulf of Mexico
Wind farm with footprint of 5km × 5km (assumed)
40-60 wind turbines
Approximate hub height of 100m
Gulf of Mexico site:(25° N LAT; 90° W LON)
Gulf of Mexico
24
Tropical Cyclone Database (HURDAT)
“Best Track” compilation of the record of hurricanes for the Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea (HURDAT)
Database includes Position of the storm center (Latitude & Longitude)
updated every 6 hours Single intensity estimate
(Max wind speed or central pressure) Saffir-Simpson scale of hurricanes
25
Track Simulation
Probabilistic vs. deterministic
Markov chain simulation (K. Emanuel et al, 2006) Constructing smooth genesis probabilities These are constructed based on genesis of database
storms; a bivariate Gaussian density estimator is chosen for the kernel (Z. Botev et al 2009)
Constructing smooth conditional transition probabilities State variables:
(i) rate of change of translational speed,(ii) rate of change of translational direction,
26
sθ
Track Simulation
Rates of change of variables are chosen to better capture the smoothness inherent in track manifolds make the track propagation less sensitive to resolution
Joint probabilities constructed from independent transitional probabilities (Markov-based) Joint probability distribution constructed in this way is too
sparse due to lack of data; thus, it is assumed that the rates of changes are independent:
Construct smooth termination probabilities
),(),(),( 11 iiiiiiii epesspsp −−= θθθ ),,( iiii zyxe =
27
Termination probabilities
In general, a termination probability depends on the storm location and its physical characteristics PDFs can be constructed using HURDAT (as with genesis PDFs)
An evolution model (Intensity model) for the storm’s physical characteristics has not yet been implemented.Two criteria have been used for storm termination: The duration of the storm is restricted to a max of 8 days If, due to lack of data, the storm can not propagate further,
it is terminated.
28
On kernel choices …
For non-parametric density estimation, various types of kernels are used (Gaussian, Epanechnikov, …)
For smoothing the marginal distributions of the transitional probability, the Epanechnikov kernel is used.
An optimal bandwidth for the kernel was first selected based on Bowman and Azzalini (1997)
29
On kernel choices …
By changing the kernels and their corresponding bandwidths the following was observed:
in order to get the best match on quantile-quantile plots, a variable bandwidth must be adopted;
the bandwidth for a region depends on the number of data in that region; generally, a larger bandwidth was needed for sparse data;
the specific choice of kernel (e.g., Gaussian vs. Epanechnikov, etc.) does not influence the results very much.
30
Genesis Probabilities
-100 -80 -60 -40 -20 05
10
15
20
25
30
35
40
45
50
Longitude
Latit
ude
HURDAT STROM GENESIS POINTS
0
10
20
30
40
50
-100 -80 -60 -40 -20
Latit
ude
Longitude
SMOOTHED GENESIS PROBABILITY
0.5
1
1.5
2
x 10-3
31
Challenges in Track Simulation
Scarce data from previous hurricanes
Creating a synthetic database by tripling the data points via linear interpolation of the storm path (6-hr 2-hr).
This results in higher resolution and smoother hurricane paths.
It also introduces some error in the analyses as it is assumed that the hurricane will not change its speed and direction during the 6-hr time intervals
32
Verification of Storm Simulations
1,000 storms simulated based on method described
For comparison of existing versus simulated storms, quantile-quantile (Q-Q) plots are generated
Q-Q plots constructed by: collecting the observed data for selected 10º × 10º sectors comparing local probability distributions of variables of
interest from simulated storms and HURDAT data
33
Simulated Storms
-120 -100 -80 -60 -40 -20 0 200
10
20
30
40
50
60
70
SimulationDatabase
Comparison of 1,000 storms—simulated versus recorded (in database)
34
Verification of Storm Simulations
-120 -100 -80 -60 -40 -20 00
10
20
30
40
50
60
70
80
Longitude
Latit
ude
400 Simulated Storms
1 2 3
6
°10
°10
35
4 5
Q-Q Plots for Translation Direction (6 Sectors)
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -85 Latitude= 25 No of Points 6347
36
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -75 Latitude= 25 No of Points 7791
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -55 Latitude= 25 No of Points 3058
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -85 Latitude= 30 No of Points 6410
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -75 Latitude= 30 No of Points 8822
-150 -100 -50 0 50 100 150
-150
-100
-50
0
50
100
150
Translational Direction (Simulation)
tran
slat
iona
l Dire
ctio
n (D
atab
ase)
Q-Q Plot Longitude= -55 Latitude= 30 No of Points 2545
1 2 3
4 5 6
0 20 40 60 80 1000
20
40
60
80
100
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -75 Latitude= 30 No of Points 8822
0 20 40 60 800
20
40
60
80
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -55 Latitude= 25 No of Points 3058
0 20 40 60 80 1000
20
40
60
80
100
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -55 Latitude= 30 No of Points 2545
0 20 40 60 80 100 1200
20
40
60
80
100
120
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -85 Latitude= 30 No of Points 6410
0 20 40 60 80 1000
20
40
60
80
100
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -75 Latitude= 25 No of Points 7791
0 20 40 60 80 100 1200
20
40
60
80
100
120
Translational Speed (Simulation)
Tra
nsla
tiona
l Spe
ed (
Dat
abas
e)
Q-Q Plot Longitude= -85 Latitude= 25 No of Points 6347
Q-Q Plots for Translation Speed (6 Sectors)
37
1 2 3
4 5 6
Remarks on the Q-Q plots
Termination criteria have not yet been rigorously implemented. Storms were simply terminated after a specified period this causes an accumulation of termination points sometimes and results in an abrupt shift in Q-Q plots for some sectors Q-Q plots show bias towards lower translation speeds for simulations (relative to recorded storms)
In sectors where data are scarce, the Q-Q plots generally show greater deviation of simulations versus recorded track parameters
38
Wind Field Simulation
Identification of those storm tracks that pass through or near the wind farm of interest
Deterministic evolution of storm intensity parameters along the paths including: central pressure difference radius of maximum wind
Wind field simulation at points along each storm track
39
( ))( ),(, kjkjkj tYHtXHH =
( ))( ),(1, ttYHttXHH kjkjkj ∆+∆++
),,,( kjniur
),,,( kjniuθ
( ) , , )()()()( nnnn zyxT =
jTrack,
X
Y
INERTIAL FRAME
<Wind farm layout>
)(,,,
nkjnkj THR =
)1(T )2(T
)( NT
Distance between turbine and hurricane center
Turbines in Wind Farm in Path of Moving Storm
General Idea of Simplified Wind Field Simulation
Solve the governing equations at “gradient height” (where friction can be neglected) by adopting a pressure field using Holland’s pressure profile parameter
Obtain the required wind velocity field through the atmospheric boundary layer by the “log law of the wall”
Calibrate the resulting model with measured (dropsonde) data
41
Simplified Wind Field SimulationGRADIENT WIND & PRESSUREpc central pressureRMW radius to max windB Holland Parameterf Coriolis parameterΨ latitude
42
0
*
22 22 1 1/ 1/* *
020
1/22
343.7 0.26
ln ;
G G G
dz l zz
V V VH f f
r r r
u uzC z c c
z guω ωκ
−
−
− ∂ = + + ⋅ + + ∂
= = =
2*
*0
( ) ln 0.4u z z
U zz Hκ
= −
,max
2 21/2
; ( ) exp ;2
1.881 0.00557 0.01097
exp4 2
B B
G c
B B
G
RMW f RMW RMWV K p p r p
r r
B RMW
RMW p RMW f r f rV B
r r
ψ
ρ
⋅ = ∆ − = + − = − ⋅ − ⋅
∆ ⋅ ⋅ = ⋅ ⋅ ⋅ − + −
INERTIAL STABILITY, BL HEIGHT, DRAGu* friction velocityz0 roughness lengthH* boundary layer ht parameterκ von Kármán constant
VICKERY ET AL, 2009—Marine Hurricane Boundary Layer Empirical Model
GN
GN
GN
( ), , : Evolving wind field of hurricaner zu u uθ
( ))( ),(, kjkjkj tYHtXHH =( ) , , )()()()( nnnn zyxT =
Demand on an Individual Wind Turbine Unit
jth hurricane track at tknth turbine location
( )( )( )knjknjizz
knjknji
knjknjirr
tztrukjniu
tztrukjniu
tztrukjniu
,),(),,,(
,),(),,,(
,),(),,,(
,,,
,,,
,,,
=
=
=
θθ
knjir ree ,,,ˆ ˆ ≡
jTrack,
knjiee ,,,ˆˆ θθ ≡
( ) , ,: )()(i
)( ni
nni zyxA
( ))( ),( ),(: kjkjkj tZHtYHtXHBABr =
),,,( kjnivX
),,,( kjnivY
),,,( kjnivZ
Transform
Radial coordinate (storm reference) Cartesian coordinate (turbine reference)
Velocity field Mapping: Hurricane reference Turbine reference
rotor plane
lateralwind, UY 63m
longitudinalwind, UX
no yaw controlno waves modeledno turbulence (yet)
y
x
Plan view showing hurricane and wind turbine
(not to scale)
storm
radialwind, Ur
tangentialwind, Uθ
track
Wind Field Simulation—Illustration
510
1520
25
25
30
30
35
35
35
3535
35
35
35
35
35
40
40
40
40
40
40
4040
45
45
45
45
45
45
45
45
X (m)
Y (
m)
-1 -0.5 0 0.5 1x 10
5
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
x 105
46
55
5
1010
10
1515
15
2020
20
2525
25
25
30
30
3030
30
3535
35
35
35
35
35
40
40
40
40
40
40
45
45
45
45
45
Radius (m)
Hei
ght (
m)
1 2 3 4 5 6 7 8 9 10 11x 10
4
100
200
300
400
500
600
700
800
900
1000
1060 ; 40 ; 30 ; 0.71 Gp mb RMW km u Vψ∆ = = = ° =
horizontal cross-section @250 m AGL
vertical cross-section azimuthallly averaged
track j=1 to no. of tracks
track time turbine grid
Extreme velocity computation
FASTWind turbine load
calculation
Estimation of damage or losses
in Wind farm
Check if hurricane is close enough to turbineto warrant demand assessment
Integrated Assessment of Wind Farm Units for Simulated Storm Tracks
time k=1 until termination pointturbine n=1 to NT (units in farm)
grid i=1 to (NG)3
48
Turbine Response Simulation—What it Entails
Turbine Response Simulation – FAST (NREL) Combined modal and multi-body dynamics formulation.
Three-bladed turbines: 9 rigid bodies (earth, support platform, nacelle,…) 5 flexible bodies (tower, three blades, and drive shaft)
24 DOFs: support platform motions (6), tower motions (4), nacelle yaw motion (1), variable rotor speed (1), etc.
Flexible Tower and Blades:
o Tower: Four mode shapes
o Blades: Two flapwise, one edgewise modes.
Layout of a conventional, upwind, three-bladed turbine (Jonkman, 2005)
49
NREL 5-MW Baseline Wind Turbine Model
Properties/Dimensions Values
Power RatingRotor Orientation, ConfigurationControlDrivetrainRotor DiameterHub heightCut-In, Rated, Cut-Out SpeedsCut-In and Rated Rotor SpeedsRated Tip SpeedOverhang, Shaft Tilt, PreconeRotor MassNacelle MassTower Mass
5 MWUpwind, 3 Blades
Variable Speed, Collective PitchHigh Speed, Multi-Stage Gearbox
126 m90 m
3 m/s, 11.4 m/s, 25 m/s6.9 rpm, 12.1 rpm
80 m/s5 m, 5o , 2.5o
110,000 kg240,000 kg347,460 kg
REpower 5.0 MW Turbine.(Photo: REpower Systems AG)
Specifications are based largely
on the REpower 5M
> 90 m
Total height > 153 m
50
An illustrative analysis of a wind turbine
Inflow Field and Turbine Response Simulation
14 x 10 m = 140 m
90 m
yz
xz
14 x 10 m = 140 m
x
Wind
51
Illustration of Rotor/Tower Load Computation
0 50 100 150-20
0
20
40
Time (seconds)
OoP
BM
(M
N.m
)
Blade Out-of-Plane Bending Moments
0 50 100 150-30
-20
-10
0
10
20
Time (seconds)
Yaw
M (
MN
.m)
Tower Yaw Moments
0 50 100 150-200
-100
0
100
200
300
Time (seconds)
FA
BM
(M
N.m
)
Tower Fore-Aft Bending Moments
Extreme8.3 MN.m
Extreme11.3 MN.m
Extreme8.2 MN.m
52
Risk Assessment or Loss Estimation
Loss or risk estimationEvaluate wind farm for each simulated storm track and associated evolving wind field.
What percent of turbines see hub-height wind speeds in excess of v* and at what frequency?
What percent of turbines in the farm experience load levels on the tower and/or rotor in excess of L* and at what frequency?
53
Concluding Remarks
This work is an attempt to solve the integrated problem of analysis of a wind farm under hurricane loads, considering probabilistic hurricane track simulation deterministic analysis of evolution of hurricane along
selected tracks (that pass through/near the farm) physical wind filed simulation along track assessment of wind speeds and loads in future, estimation of “losses”
Model extension and future work to include: storm track termination fuller validation of simulated versus recorded storm tracks coupled wind-wave input
54
Acknowledgments
Financial Assistance continuing support for examine off-standard/anomalous loads on wind turbines such as microbursts, low-level jetsSandia National Laboratories(Contract No. 743358; Manager: Mr. Joshua Paquette)
Assistance on slides from:Mr. Jinkyoo Park (MS Student)Mr. Hieu H. Nguyen (PhD Student)
55
Thank you!e-mail: [email protected]; ph: 512.232.5691]
Questions and Answers
56
www.DeepCwind.org
UMaine DeepCwind Consortium Update
Prof. H. J. Dagher, Ph.D., P.E, Principal Investigator, [email protected] Anthony Viselli, P.E., Research Engineer (Presenter)
Robert Lindyberg, Ph.D.. P.E., Project ManagerProf. Heather Dease, Ph.D.Prof. Peter A. Jumars, Ph.D.Prof. Melissa Landon, Ph.D.Prof. Andrew Goupee, Ph.D.
AWEA WINDPOWER 5-25-11
UMaine DeepCwind Consortium DOE Contract January 2010
35 partners
Dr. H. J. Dagher, (207) 581-2138 [email protected]
Maine: 5GW Floating by 2030• Maine’s 5GW by 2030 plan• $/kWh Can Floating Turbines Compete?• DeepCwind Technology Roadmap
– Floating Design Competition– 1:50 Scale Testing (April-May 2011)– 1:3 Scale Testing (July 2012, 2013)– 25 MW Pilot Farm (2017)– 0.5-1GW Floating Farm (2020)
• UMaine Deepwater Test Site• UMaine Offshore Wind Laboratory
Dr. H. J. Dagher, (207) 581-2138 [email protected]
Outline• DeepCwind Technology Roadmap
– Floating Design Competition– 1:50 Scale Testing (April-May 2011)– 1:3 Scale Testing (July 2012, July 2013)– 25 MW Pilot Farm (2017)– 500-1000MW project (2020)
• Integrated Deepwater Offshore Wind Labs:– UMaine Offshore Wind Lab– UMaine Monhegan Test Site
Dr. H. J. Dagher, (207) 581-2138 [email protected]
Floating Design Competition14 Designs Received
Tension-Leg PlatformsSparsSemi-Submersibles
Three 1:50 Scale Tests
62
Semi-Submersible Spar TLP
OC3 Hywind tower, NREL 5MW Turbine
Simulator Refinement PlanModel improvements will focus on nonlinear hydrodynamic and mooring physics:• Viscous effects (from Morison’s equation)• Second order wave diffraction effects• Mooring line dynamics (FEM)
Left image: http://www.wamit.com/structures.htm Right image courtesy of The Glosten Associates
WAMIT® Second OrderAnalysis Mesh for a TLP
Wind turbine TLP design: A concept that requires all the noted model improvements
Scaling Methodology Parameter Unit Scale Factor
Length L •Area L2 • 2
Volume L3 • 3
Mass M • 3
Wave Celerity LT-1 • 0.5
Wave Height L •Wave Amplitude L •
Wave Length L •Wave Period T • 0.5
Wind Speed LT-1 • 0.5
Wind, Wave Force MLT-2 • 3
Power ML2T-3 • 7/2
Stress ML-1T-2 •MOE ML-1T-2 •
Scaling Laws: • Froude scaling• Tip Speed Radio, TSR• Reynolds # issues
Scale Model Parameters
65
5MW 1:50 Scale
Blade weight 35,000 lbs 0.31 lb
Blade length 185 ft 4.04 ft
Hub mass 114,000 lbs 1.00 lb
Nacelle mass 480,000 lbs 4.23 lb
Total turbine mass (rotor, blades &
nacelle)750,000 lbs 6.17 lb
Scaled Wind Environment
Custom Wind Generator:• Multiple fans, screens
and nozzle• Eliminates swirl• Reduces turbulence
intensity• Rotor aerodynamics
properly modeled
Model Turbine: Booth 4241A
1:50 TLP Basin Test (4/11)
Wave-Wind Basin Test MatrixHs
Wave (m)
Operation 1yr 100yr
Wind Speed (m/sec) 2.0 7.1 10.5
Operation 1 7.00 xOperation 2 9.00 xOperation 3 11.40 x xOperation 4 16.00 xOperation 5 21.00 x x
100 yr 30.50 x
1:50 Scale Tests of 5 MW
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FAST/Test ComparisonsPSD for TLP tower-top force in surge direction
The FAST simulator tower top force does not have any energy in the tower bending/platform pitching range.
Wave energy response
Platform pitch/tower bending due to:
mooring dynamicsand 2nd order diffraction
3P
1P
The UMaine Offshore Wind Laboratory$36 Million addition, Opens Oct 2011
• Nanocomposites• Robotics manufacturing• Blade Testing 70m• 250 ft long reaction floor• Wave-wind basin• Durability
UMaine Deepwater Wind Test SitePrototype TLP July 2012
Dr. H. J. Dagher, (207) 581-2138 [email protected]
27mRotor
60-days Hardware
Permits
Deployment: July-Oct 2012
74
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec52% 69% 56% 47% 28% 7% 0% 1% 4% 46% 58% 73%
Probability of Exceeding 1:3 Scale, 50-Year Significant Wave Height (3m) in One Week
Instrumentation Plan for 1:3 Scale
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Bladestresses, loads
Connectionloads
Anchor loads
TowerStresses, moment and shears along height
Platform loadsstresses, moment and shears along depth
Mooringloads
6 DOF accelerometer at CG
Grid Simulator
School of Marine Sciences
Birds, Bats, Fish, Marine Mammals
• Year-round bird-bat radar• Ambient noise to assess
turbine source levels and frequencies and sound fields; attention to frequencies known to be used by marine mammals
• Marine mammal surveys, regional sightings data
• Fish monitoring, benthic invertebrates
Maine 25 MW Pilot Floating Farm
Dr. H. J. Dagher, (207) 581-2138 [email protected]
Sept 1, 2010 – RFP Issued May 2, 2011 – Response were due20-year PPA 2012 PUC selects developer2017 Complete construction2020 Grow to > 500MW Farm
Summary• Maine’s 5GW by 2030 plan• 10 cents/kWh by 2020• DeepCwind Technology Roadmap
– Floating Design Competition– 1:50 Scale Testing (April-May 2011)– 1:3 Scale Testing (July 2012, July 2013)– 25 MW Pilot Farm (2017)– 500-1000MW project (2020)
• Integrated Deepwater Offshore Wind Labs:– UMaine Offshore Wind Lab– UMaine Monhegan Test Site
Dr. H. J. Dagher, (207) 581-2138 [email protected]
Booth 4241
Thank you for your attention.Questions?
5/25/2011