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Model Predictive Control for Wind Turbines

Model Predictive Control for Wind Turbines. Arne Koerber Rudibert King. Agenda. Model Predictive Control Overview Model Controller Trade-Off Preview Control. past. future. Predicted Output. Set-point. Output (e.g. generator speed). Control variables (e.g. pitch command). k+2. k.

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Model Predictive Control for Wind Turbines

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  1. Model Predictive Control for Wind Turbines Arne Koerber Rudibert King

  2. Agenda Model Predictive Control Overview Model Controller Trade-Off Preview Control

  3. past future Predicted Output Set-point Output (e.g. generator speed) Control variables (e.g. pitch command) k+2 k k+1 k+l Prediction horizon Model Predictive Control (MPC) Originated in process industry Also known as „receding horizon control“ At every time step: • Predict output trajectory using system model • Calculate optimal control sequence • Apply first control input

  4. MPC for Wind Turbines Key Benefits: • Direct MIMO formulation • Tuning in terms of relevant quantities • Optimality not lost during operation at constraints (e.g. pitch rate limits) • Knowledge of future wind can be included

  5. Turbine Model Model fidelity crucial for MPC performance and computational effort Simplified Model derived from higher order model (Flex5) Aerodynamic and drive train model: Coupled with 2 DOF (tower+blades) structural model through effective wind speed

  6. Control Structure 2 separate estimators: • EKF to estimate effective wind speed • Linear state and disturbance estimator for offset free control (integral action) Only collective pitch control

  7. Rotor speed control Pitch activity Tower loads Trade-off and results Main trade-off for full load operation decreasing pitch activity Tower loads w.r.t baseline Exemplary results for a generic 3 MW turbine at 17m/s mean wind speed Baseline: Classical controller + tower damper Gen speed deviation w.r.t baseline MPC slightly more effective than baseline

  8. Preview Control Controller „knows“ effective wind speed several seconds ahead e.g. from • Lidar measurements • Other turbines of farm • Prediction Model Wind speed information can be included directly in the MPC optimization problem Assumption: Perfect measurement of effective wind speed 5 seconds ahead

  9. Preview Control Gust Results Preview MPC control for a Mexican Hat type gust Gen. Speed deviation: -80% Max Tower base moment: -30% No additional pitching Wind speed Pitch angle Gen. speed Tower Base moment

  10. Preview Control ResultsTurbulent Conditions Preview control also has benefits for power production operation At the same pitch activity level and same allowable gen. speed deviation: Tower base DEL: -17% Tower loads w.r.t baseline Gen speed deviation w.r.t baseline

  11. Summary MPC has several benefits for wind turbines One linear implementation introduced Slightly more effective than a baseline controller Significant benefits in combination with preview control

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