offshore wind turbine operation under atmospheric icing
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Offshore Wind Turbine Operationunder Atmospheric Icing and
Controller System FaultsMahmoud Etemaddar
PhD Candidate, CeSOS28 May 2013
CeSOS Conference HighlightsDepartment of Marine Technology
NTNU-Trondheim Norway
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Outline
• Introduction• Wind Turbine Operational Conditions• Wind Turbine Operation under Atmospheric Icing• Wind Turbine Operation under Fault Condition• Conclusions
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Deep Water Offshore Wind
Economic Production Cost WT Life Time : 20 Years
Introduction
Vast, Reliable, Economic
Primary challenge
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Operational Conditions
100 Years Extreme WindIce Impact
EarthquakeLightning
Atmospheric IcingSystem Faults
20 Years Life Time
Normal Abnormal
Power ProductionStart Up
Shut DownDirection Change
Wind GustIdling
Rarely happeningStrongly Stochastic
Complicated PhysicsLimited KnowledgeLack of experience
Frequently happeningWeakly Stochastic
Relatively Simple PhysicsDeveloped Knowledge
Experience from onshore
More Research
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Operational Conditions
All Wind TurbinesReduce the availability
Increase the Fatigue DamageExtreme Loads
Reduce the Life Time
2 Abnormal Operational Conditions
Atmospheric Icing Sub-System Faults
Wind Turbines in Cold Climate RegionsOperational Challenge
Aerodynamic DegradationVibration
Power Reduction
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Atmospheric Icing
When is there a risk of atmospheric icing for offshore Wind Turbines ?
• Wherever there is sea icing !• Temperature bellow zero degree + Super cold water particles :
Fog, Rainfall, Wet snow, Sea spray
2 Examples of Offshore Wind Sites:
Gulf of Bothnia (Finland)Greenland Sea (Canada)
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Atmospheric Icing Problem
1. Modeling the Atmospheric Icing 2. Aerodynamic Degradation3. Response Realization
Estimate Envi. Param.LWC, MVD, Temp, RH
Calculate Sys Param.AOA, RW, 2D Airfoil
To simulate icingIce Profile, Distribution
• HAWC2 ServoAeroElasic• NREL 5MW Reference• WS [3-25] m/s
• Langmuir A Icing model• Constant Parameters• Potential Flow Solver• Particle Trajectory
• T=[-20:-5], • LWC=[0,05:0,15]• MVD=[10:22] • RH= 100 %
Aerdynamic Calculationwith CFD
• K-Eps Turbulent Model• Transient Solver• Wind Tunnel Test • Surface Roughness
Modify the HAWC2 Aero. And St. Input Files
Simulate the Responseand post processing
• Extrapolation of Aerodynamics• Ice mass distribution
• Normal Operation• Shut Down • Parking
HAWC2
LEWICE
FLUENT
HAWC2
4 hours ice profile
3 main steps to study:
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Effect of Icing on Airfoil Aerodynamics
CL: Lift Coefficients CD : Drag Coefficients
NACA64-618
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Bellow Rated : 0 Deg Blade Pitch
Effect of Icing on Rotor Aerodynamics
Above Rated :10 Deg Blade Pitch
Reduces Reduces
POWER THRUST
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Bellow rated : 0 Deg Blade Pitch Above rated :10 Deg Blade Pitch
Reduces
Increases
Effect of icing on Rotor Aerodynamics
POWER
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Effect of Icing on Blade Mass Distribution
Total Ice Mass = 486 Kg2.8 % of total mass of blade
24 Hours Icing
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Wind Turbine Responses under Icing
Test Case : 24 Hrs working under time varying atmospheric icing
• NREL 5MW• 1,2, 3 Blades Icing
Power Production Extreme Gust Normal Shut Down Emergency Shut Down Parking Condition
NREL 5MW OC3-HYWIND (SPAR)
LAND BASED FLOATINGOFFSHORE
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Effect of Icing on Extreme ResponsesIEC DLC 1.1 ( Power Production )
FLOATING OFFSHORELANDBASED
TB : Tower BottomSB : Shaft BearingBi : Blade Root
Iced Wind Turbine
Clean Wind Turbine
TOWER SHAFTSHAFT
1 BLADE ICED2 BLADES ICED3 BLADES ICED
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IEC DLC1.1 (Power Production)
FATIGUE DAMAGE ON TOWER BOTTOM OF LANDBASED WIND TURBINE
Effect of Icing on Fatigue Damage
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Effect of Icing on Floater Response
HEAVE YAW
1P
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Wind Turbine Operation under Fault
WIND TURBINE FAULT
SYSTEM CHARACTERISTICS
FAULT AND FAILURE DATA
SYSTEM
SUB SYSTEM
SUB ASSEMBLY
COMPONENTS
ASSEMBLY
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
LEVEL 5
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Wind Turbine Failure Data
• Pitch System (Rotor)• Converter (Power)• Yaw System (Nacelle)
RESULTS RELIAWIND PROJECT
Con
tribu
tion
to T
otal
Fai
lure
/ WT
/ yea
r
Most Unreliable Components :
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Wind Turbine Subsystem Faults
2 FAULTS IN PITCH CONTROLLER
ACTUATOR SENSOR
STUCK
RUNAWAYMULTIPLICATIVE GAIN
FIXED VALUEADDITIVE GAIN
PITCH SENSOR ADDITIVE GAIN
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Effect of Faults on Operational Parameter
PITCH ACTUATOR STUCK
Bellow Rated
Above Rated
Bellow Rated
Above Rated
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Effect of Faults on Extreme Responses
IDE DLC 1.1 (POWER PRODUCTION)TB : Tower BottomSB : Shaft BearingBi : Blade No. i
Fault Response
Fault Free Response
OFFSHORELANDBASEDPITCH ACTUATOR STUCKPITCH SENSOR ADDITIVE GAINPITCH SENSOR FIXED VALUE
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Effect of Faults on Floater Responses
SURGE YAW
NON FAULT
PITCH ACTUATOR STUCK
PITCH SENSOR ADDITIVE FAULTPITCH SENSOR FIXED VALUE
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Conclusions:
• In below rated wind speed power-shaft_speed consistancy and in aboverated wind speed thrust-shaft_speed consistancy can be used as icedetection criteria• The deicing system is mainly needed for outer half of the blade• Aerodynamic degradation and power reduction are the main challenges• Shaft is the most sensitive component for both onshore and offshore wind turbines• DLC 1.4 : EDC was the critical load case for the offshore wind turbine
Wind Turbine Response Under Atmospheric Icing Condition
Wind Turbine Response under Fault Condition• Pitch sensor fixed value fault was the most critical fault case • Shaft was the most sensitive structurtal member• Effects of faults were more severe on land based wind turbine than offshore floating• Yaw response was the most affected DOF for the SPAR wind turbine
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References:
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1. Etemaddar M., Blanke M. and Moan T., Response Analysis and Comparison of a Spar Type and a Land-based Wind Turbinesunder Blade Pitch Controller Faults. Wind Energy (Submitted), 2013.2. Etemaddar M., Hansen M. O. L. and Moan T. Atmospheric Ice Accumulation and its Effect on a Typical 5-MW Wind Turbine inDifferent Operational Conditions. in EWEA Offshore Wind 2011. Amesterdam, Netherland 29Nov-1Dec.3. Etemaddar M., Hansen M. O. L. and Moan T., Wind turbine aerodynamic response under atmospheric icing conditions. Journalof Wind Energy, DOI : 10.1002/We. 1573, 2012.4. Bachynski E. E., Etemaddar M., Kvittem M. I., Luan C. and Moan T., Dynamic Analysis of Floating Wind Turbines DuringPitch Actuator fault, Grid Loss and Shutdown, in 10th Deep Sea Offshore Wind R&D Conference, DeepWind 20132013: Norway,Trondheim Jan 24-25.5. Makkonen L., Atmospheric icing on sea structures, 1984, COLD REGIONS RESEARCH AND ENGINEERING LABHANOVER, Document No. ADA144448, APR 1984.6. Makkonen L., Laakso T., Marjaniemi M. and Finstad K.J., Modelling and prevention of ice accretion on wind turbines. Journalof Wind Engineering, 2001. 25(1): p. 3-21.7. Virk M. S., Homola M. C. and Nicklasson P. J., Effect of rime ice accretion on aerodynamic characteristics of wind turbineblade profiles. Wind Engineering, 2010. 34(2): p. 207-218.8. Esbensen T. and Sloth C., Fault Diagnosis and fault-Tolerant Control of Wind Turbines, in Engineering Science andTechnology2009, AALBORG University.9. Hameed Z., Hong Y. S., Cho Y. M., Ahn S. H. and Song C. K., Condition monitoring and fault detection of wind turbines andrelated algorithms: A review. Renewable and Sustainable energy reviews, 2009. 13(1): p. 1-39.10. Odgaard P. F.; Stoustrup J. and Kinnaert M., Fault tolerant control of wind turbines: a benchmark model, in 7th IFACSymposium on Fault Detection, Supervision and Safety of Technical Processes 2009: Barcelona, Spain, June 30 - July 3.11. Bussel V., Z.M.B., Reliability, availability and maintenance aspects of large-scale offshore wind farms, a concepts study, inMarine renewable Energy 2001: London UK, Mar 3-5. p. 119-126.12. Hahn B., Durstewitz M. and Rohrig K., Reliability of wind turbines–Experience of 15 years with 1500 WTs, in Institut furSolare Energieversorgungstechnik (ISET), Kassel, Germany2006.13. Tavner P. J., Xiang J. and Spinato F., Reliability analysis for wind turbines. Wind Energy, 2007. 10(1): p. 1-18.14. Wilkinson M., Harman K., Hendriks B. and Spinato F. Measuring Wind Turbine Reliability, Results of the Reliawind Project.in European Wind Energy Conference. 2011. Amesterdam, Netherland , 29 Nov- 1 Dec.
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Thanksfor Your Attention
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