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On using Pareto optimality to tune a linear model predictive controller for wind turbines

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  • Odgaard, Peter Fogh
  • Larsen, Lars F.S.
  • Wisniewski, Rafael
  • Hovgaard, Tobias Gybel

Abstract

Optimal operation of wind turbines is important in order to minimize cost of energy, which is one of the major focus areas of the wind industry. Model predictive control (MPC) is a candidate for a control solution which effectively balances the different and potentially conflicting objectives, e.g. generated power and structural loads. This article presents a method on how to tune multi-objective MPC problems using Pareto curves. The approach is applied to a realistic wind turbine MPC problem, in which a joint power and tower fore-aft fatigue load optimization is performed. The controller is evaluated on a high fidelity model using a Vestas wind turbine simulator. In addition to the multiple control objectives, a number of constraints are considered as well. The evaluation shows a good potential of using model predictive control for this problem compared with an industrial baseline controller as, it approximately obtains the same mean generated power, while lowering the tower fore-aft fatigue loads. The computed Pareto curves of the trade-off between tower fore-aft fatigue load and mean generated power for a number of different weight matrices, demonstrate a potential tool for tuning MPC solutions for a wind turbine.

Suggested Citation

  • Odgaard, Peter Fogh & Larsen, Lars F.S. & Wisniewski, Rafael & Hovgaard, Tobias Gybel, 2016. "On using Pareto optimality to tune a linear model predictive controller for wind turbines," Renewable Energy, Elsevier, vol. 87(P2), pages 884-891.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:884-891
    DOI: 10.1016/j.renene.2015.09.067
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    Cited by:

    1. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2019. "Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 131(C), pages 549-562.
    2. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    3. Lin, Zhongwei & Chen, Zhenyu & Wu, Qiuwei & Yang, Shuo & Meng, Hongmin, 2018. "Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis," Energy, Elsevier, vol. 147(C), pages 812-825.
    4. Moghadasi, Amirhasan & Sarwat, Arif & Guerrero, Josep M., 2016. "Multiobjective optimization in combinatorial wind farms system integration and resistive SFCL using analytical hierarchy process," Renewable Energy, Elsevier, vol. 94(C), pages 366-382.
    5. Yuan, Yuan & Chen, Xu & Tang, J., 2020. "Multivariable robust blade pitch control design to reject periodic loads on wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 329-341.
    6. Li, Liang & Gao, Yan & Hu, Zhiqiang & Yuan, Zhiming & Day, Sandy & Li, Haoran, 2018. "Model test research of a semisubmersible floating wind turbine with an improved deficient thrust force correction approach," Renewable Energy, Elsevier, vol. 119(C), pages 95-105.
    7. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    8. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    9. El-Baklish, Shaimaa K. & El-Badawy, Ayman A. & Frison, Gianluca & Diehl, Moritz, 2020. "Nonlinear model predictive pitch control of aero-elastic wind turbine blades," Renewable Energy, Elsevier, vol. 161(C), pages 777-791.
    10. Cao, Yankai & Zavala, Victor M. & D’Amato, Fernando, 2018. "Using stochastic programming and statistical extrapolation to mitigate long-term extreme loads in wind turbines," Applied Energy, Elsevier, vol. 230(C), pages 1230-1241.

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