Control Of Offshore Windmills

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Control Of Offshore Windmills

  1. 1. 1 Control of offshore windmills Rambabu Kandepu
  2. 2. 2 Contents • Introduction • Physics • Classification of wind turbines • Offshore wind turbines • Control of wind turbines
  3. 3. 3 Introduction • Source of wind energy – The sun is source of all renewable energy (except tidal and geothermal power) – The earth receives 1.74 ×1014 kWH energy per hour from sun – 1-2% of this energy converted to wind energy – Wind energy > 50 to 100 times biomass energy
  4. 4. 4 Introduction • Advantages of wind energy – Clean energy – Widely distributed – Generation at local level – Low risk – Energy diversity
  5. 5. 5 Introduction History – Wind power to Mechanical power (Persia, Tibet and China, 1000 AD) – Wind power to Electrical power (Dane Poul LaCour, 1891)
  6. 6. 6 Introduction Wind energy conversion systems – Aerodynamic drag – Aerodynamic lift • Vertical axis (VAWT) (ex., Darrieus trubine) • Horizontal axis (HAWT)
  7. 7. 7 Introduction - components • Rotor blades • The hub • Gearbox • Generator • Nacelle. • Tower.
  8. 8. 8 Introduction • Wind turbines – Downwind • The wind passes the tower first before the rotor • “free-yaw” wind turbine – Upwind • the wind impinges upon the rotor first • “yaw-driven” machine
  9. 9. 9 Introduction • Power – Wind force to torque • Wind energy transferred – density, – wind velocity – rotor area
  10. 10. 10 Introduction – power curve The Physics 1 Power in wind = ρ AV 3 (watts), 2 where ρ = air density (kg m -3 ) V = wind speed (m s -1 ) A = the intercepting area (m 2 ) Maximum power extraction is 59% (Betz)
  11. 11. 11 Introduction – power curve • Cut-in wind speed • Rated wind speed (12-16 m/s) • Cut-out wind speed (20-25 m/s) • Depends on air pressure, aerodynamic shape of rotor, placing of turbine
  12. 12. 12 Introduction – power curve • Power produced 1 P = ρ ACPV 3 2 • Tip speed ratio ωR λ= V
  13. 13. 13 Wind turbine topologies Fixed speed Variable speed Max. efficiency at one Max. efficiency over a wide particular wind speed range of wind speeds Simple, robust, reliable and Increased energy capture, well proven improved power quality, reduced mechanical stress Mechanical stress, limited Losses in power electronics, power quality control more components, increased cost
  14. 14. 14 Types of power control • Stall control (passive control) – The blades are bolted to the hub at a fixed angle – Design of rotor aerodynamics causes the rotor to stall – Simplest, robust and cheapest – Lower efficiency at low wind speeds, no assisted start up
  15. 15. 15 Types of power control • Pitch control (active control) – Blades can be turned out or into the wind – Extra complexity and higher fluctuations at high wind speeds – Good power control, assisted startup and emergency stop – Requires fast response control loop – additional costs and fatigue loading increases
  16. 16. 16 Types of power control • Active stall control – The stall of the blade is actively controlled by pitching the blades – Smoother power control without high power fluctuations – Easier to carry out emergency stops and to start up
  17. 17. 17 Offshore wind turbines • More faster and uniform wind •The most critical aspect is the substructures. • Over 11 GW of new offshore wind projects are planned before the year Technology progression of offshore wind turbines 2010
  18. 18. 18 Offshore wind turbines Typical cost breakdown of an offshore wind plant in shallow water
  19. 19. 19 Offshore substructures Cost of Offshore Wind Turbine Substructures with Water Depth
  20. 20. 20 Shallow water foundations • 5-18m deep • Monoplies – 160 MW wind farm at Horns Rev (Denmark) – Simple and minimal design – Depth limited due inherent flexibility • Gravity based – 160 MW Nysted project (Denmark) – Overcome flexibility – Increase of cost with water depth • Suction bucket foundations – Not yet used
  21. 21. 21 Transitional technology 1) tripod tower, 2) guyed monopole, 3) full-height jacket (truss), 4) submerged jacket with transition to tube tower, 5) enhanced suction bucket or gravity base.
  22. 22. 22 Transitional technology • Two wind turbine generators near the Beatrice Oil Field, North sea. • 22km offshore • Water depth off approximately 45 meters • 87 meters above sea level • 5 MW for each turbine
  23. 23. 23 Floating technology • The Spar-buoy concept, stability by ballast • The Tension Leg Platform (TLP), stability by mooring line tension • The barge concept, stability through its waterplane area
  24. 24. 24 Floating technology challenges • Turbulent winds • Irregular waves • Gravity / inertia • Aerodynamics • Hydrodynamics • Elasticity • Mooring dynamics • Control system • Fully coupled
  25. 25. 25 Floating technology – Hywind • Power > 5MW Hydro currently has a license to • Height above sea 80m place a demonstration turbine • Rotor diameter 120m offshore near Karmøy • Water depth – 200-700m
  26. 26. 26 Control of wind turbine • Wind turbines are relatively simple compared with complex electrical power plants • The stochastic nature of the wind introduces complexity • Control system objectives – Improve energy capture – Keep the power output and rotor speed with in design limits – Reduce structural dynamic loading • Offshore wind turbine – More dynamics – Vertical stability in case of floating turbines
  27. 27. 27 Control of wind turbine • Classical control – PI controllers – Multiple control loops, dynamics unknown • Modern control – Pole placement – Linear Quadratic Control – H ∞ control – Adaptive control – Disturbance Accommodating Control (DAC) – Model Predictive Control
  28. 28. 28 Control of offshore wind turbine P CP = ⎛1 ⎞ ⎜ ρ Av3 ⎟ ⎝2 ⎠ λ is the tip-speed ratio
  29. 29. 29 Wind model • Wind field varies both in space and time – A slowly varying component with a “mean” wind speed – A slowly varying “wind shear” component – A rapidly varying “turbulent” wind component
  30. 30. 30 Aerodynamics of blade section ⎛1 ⎞ FL = ⎜ ρ AW 2 ⎟ CL ⎝2 ⎠ ⎛1 ⎞ FD = ⎜ ρ AW 2 ⎟ CD ⎝2 ⎠ CL , CD - lift and drag coeff. depend on α (AoA)
  31. 31. 31 Control of offshore wind turbine • Variable speed turbine with pitch control • Four independent control inputs – Three blade pitch inputs – Generator torque command • Regulated variables – Power captured – Mechanical loads on turbine structure
  32. 32. 32 Control of offshore wind turbine • State-of-the-art in control – Control below rated speed – Control above rated speed – Drive train damping
  33. 33. 33 Control of offshore wind turbine • Control below rated speed – Objective is maximize power capture – Torque control – Pitch angle is maintained constant
  34. 34. 34 Control of offshore wind turbine • Control above rated wind speed – Objective is keep power output and loads on turbine structure within design limits – Collective pitch control – Keep generator torque constant
  35. 35. 35 Control of offshore wind turbine • Drive train damping – Serious impact on gear box Jθ (t ) + Kθ (t ) = Taero − Tgen θ - angular displacement Taero - aerodynamic torque Tgen - generator torque J - lumped inertia K - lumped stiffness
  36. 36. 36 Control of offshore wind turbine • Decentralized control – Below rated speed – torque control – Above rated speed – pitch control – Advantage in absence of reliable wind speed information – Easy to implement and tune the control algorithms – Simple to design for SISO case
  37. 37. 37 Control of offshore wind turbine • Dynamics using 5 DOF model – Blade flap – Blade edge – Tower fore-aft – Tower side-to-side – Drive train torsion • Coupled dynamics
  38. 38. 38 Control of offshore wind turbine • Advanced control design is necessary – The neglected coupled dynamics cause problems • Present research – State space control design – Pole placement, LQR, H ∞control, adaptive control, Disturbance Accommodating Control (DAC), MPC – State estimator
  39. 39. 39 Control of offshore wind turbine • DAC control – Augment states with disturbances • H ∞Control – Include uncertinitoes • MPC – Include constraints (generator power, shaft speed, limits on DOFs) • Adaptive control – Updating model parameters • Controller complexities Vs implementation
  40. 40. 40 Control of offshore wind turbine • Use of nonlinear models – Present research focuses on linearized models • Vertical stability in case of floating turbines – Tension in mooring lines
  41. 41. 41 Conclusions • Classical control – Decentralized control • Present research – State feed back control – Linearized models • Challenges – Complex dynamics – Unknown disturbances – Design issues
  42. 42. 42 Thank you for your attention ☺
  43. 43. 43 References • Keld Hammerum, A Fatigue Approach to Wind Turbine Control, Master Thesis, Technical University of Denmark • Alexandra Bech Gjørv, Hywind - Floating wind power production, Hydro • Beatrice Wind Farm Demonstrator, Project Scoping Report, Talisman Energy • Shashikanth Suryanarayanan and Amit Dixit, Control of Large Wind Turbines: Review and Suggested Approach to Multivariable Design • W. E. Leitheat and B. Connor, Control of variable speed wind turbines: design task, Int. J. Control, 2000, VOL. 73, NO. 13, 1189-1212 • William E. Leithead and Sergio Dominguez, Coordinated Control Design for Wind Turbine Control Systems • E.N. Wayman, P.D. Sclavounos, S. Butterfield, J. Jonkman, and W. Musial, Coupled Dynamic Modeling of Floating Wind Turbine Systems, Offshore Technology Conference Houston, Texas May 1–4, 2006 • Alan D. Wright, Mark J. Balas, Design of State-Space-Based Control Algorithms for Wind Turbine Speed Regulation, Transactions of the ASME, Vol. 125, November, 2003 • W. Musial, S. Butterfield and B. Ram, Energy from Offshore Wind, Offshore Technology Conference, Houston, Texas, May 1–4, 2006 • S. Butterfield, W. Musial, J. Jonkman and P. Sclavounos, Engineering Challenges for Floating Offshore Wind Turbines, Offshore Wind Conference, Copenhagen, Denmark, October 26–28, 2005 • Alan D. Wright, Modern Control Design for Flexible Wind Turbines, Technical report, July 2004. • Danish wind industry association, (http://www.windpower.org) • Lars Christian Henriksen, Model Predictive Control of a Wind Turbine, Master's thesis, Informatics and Mathematical Modelling, Technical University of Denmark, DTU

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