tension leg spar-type offshore wind turbine with upwind or ...€¦ · however, some small...

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

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

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.

Floating wind turbines concepts Spar, TLP, Semi-submersible

Tension Leg Spar (TLS) similar to SWAY

Downwind/upwind

HAWC2, USFOSModeling and theory

Hydrodynamics DLL interface for mooring system action,

Full modeling of mooring systems

Structure

AerodynamicsControl

Aero-hydro-servo-elastic

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

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).

Wave-induced vs. wave-wind-induced

mean of the responses are wind-induced and the standard deviations of the responses are primarily wave-induced

Rotor configuration upwind vs. downwind

Rotor configuration (cont.) upwind vs. downwind

The rotor configuration (tower shadow) have limited effects on the power.

Turbulence has some influence on the quality of power production (STD).

6 8 10 12 14 16 18

Mean wind speed (m/sec)

CMS-offshoreNREL-onshoreTLS-offshore

Advanced controlmay help!?

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

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 Author – Centre for Ships and Ocean Structureswww.cesos.ntnu.no Madjid Karimirad -May-2011

Thanks for your attention

Manjil (100 MW), Iran

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

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)

(6)(7)

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

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

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,

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 =

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.

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)

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.

Genesis Probabilities

-100 -80 -60 -40 -20 05

Longitude

HURDAT STROM GENESIS POINTS

-100 -80 -60 -40 -20

Longitude

SMOOTHED GENESIS PROBABILITY

x 10-3

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

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

Simulated Storms

-120 -100 -80 -60 -40 -20 0 200

SimulationDatabase

Comparison of 1,000 storms—simulated versus recorded (in database)

Verification of Storm Simulations

-120 -100 -80 -60 -40 -20 00

Longitude

400 Simulated Storms

Q-Q Plots for Translation Direction (6 Sectors)

-150 -100 -50 0 50 100 150

Translational Direction (Simulation)

l Dire

Q-Q Plot Longitude= -85 Latitude= 25 No of Points 6347

-150 -100 -50 0 50 100 150

l Dire

-150 -100 -50 0 50 100 150

l Dire

-150 -100 -50 0 50 100 150

l Dire

-150 -100 -50 0 50 100 150

l Dire

-150 -100 -50 0 50 100 150

l Dire

0 20 40 60 80 1000

Translational Speed (Simulation)

0 20 40 60 800

0 20 40 60 80 1000

0 20 40 60 80 100 1200

0 20 40 60 80 1000

0 20 40 60 80 100 1200

Q-Q Plots for Translation Speed (6 Sectors)

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

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

( ))( ),(, kjkjkj tYHtXHH =

( ))( ),(1, ttYHttXHH kjkjkj ∆+∆++

),,,( kjniur

),,,( kjniuθ

( ) , , )()()()( nnnn zyxT =

jTrack,

INERTIAL FRAME

nkjnkj THR =

)1(T )2(T

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

Simplified Wind Field SimulationGRADIENT WIND & PRESSUREpc central pressureRMW radius to max windB Holland Parameterf Coriolis parameterΨ latitude

22 22 1 1/ 1/* *

343.7 0.26

dz l zz

V V VH f f

u uzC z c c

z guω ωκ

− ∂ = + + ⋅ + + ∂

( ) ln 0.4u z z

U zz Hκ

2 21/2

; ( ) exp ;2

1.881 0.00557 0.01097

exp4 2

RMW f RMW RMWV K p p r p

RMW p RMW f r f rV B

⋅ = ∆ − = + − = − ⋅ − ⋅

∆ ⋅ ⋅ = ⋅ ⋅ ⋅ − + −

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

( ), , : 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

,),(),,,(

knjir ree ,,,ˆ ˆ ≡

jTrack,

knjiee ,,,ˆˆ θθ ≡

( ) , ,: )()(i

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)

Plan view showing hurricane and wind turbine

(not to scale)

radialwind, Ur

tangentialwind, Uθ

Wind Field Simulation—Illustration

-1 -0.5 0 0.5 1x 10

Radius (m)

1 2 3 4 5 6 7 8 9 10 11x 10

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

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)

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

An illustrative analysis of a wind turbine

Inflow Field and Turbine Response Simulation

14 x 10 m = 140 m

Illustration of Rotor/Tower Load Computation

0 50 100 150-20

Time (seconds)

Blade Out-of-Plane Bending Moments

0 50 100 150-30

Time (seconds)

Tower Yaw Moments

0 50 100 150-200

Time (seconds)

Tower Fore-Aft Bending Moments

Extreme8.3 MN.m

Extreme11.3 MN.m

Extreme8.2 MN.m

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?

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

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)

Thank you!e-mail: lmanuel@mail.utexas.edu; ph: 512.232.5691]

Questions and Answers

www.DeepCwind.org

UMaine DeepCwind Consortium Update

Prof. H. J. Dagher, Ph.D., P.E, Principal Investigator, hd@umit.maine.edu 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 hd@umit.maine.edu

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

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

Floating Design Competition14 Designs Received

Tension-Leg PlatformsSparsSemi-Submersibles

Three 1:50 Scale Tests

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

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

The video associated with this slide is not available.

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

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

27mRotor

60-days Hardware

Permits

Deployment: July-Oct 2012

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

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

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

Booth 4241

Thank you for your attention.Questions?

5/25/2011

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