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Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System

Author

Listed:
  • Hongwei Li

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Kaide Ren

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Shuaibing Li

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Haiying Dong

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

To deal with the randomness and uncertainty of the wind power generation process, this paper proposes the use of the clustering method to complement the multi-model predictive control algorithm for active power control. Firstly, the fuzzy clustering algorithm is adopted to classify actual measured data; then, the forgetting factor recursive least square method is used to establish the multi-model of the system as the prediction model. Secondly, the model predictive controller is designed to use the measured wind speed as disturbance, the pitch angle as the control variable, and the active power as the output. Finally, the parameters and measured data of wind generators in operation in Western China are adopted for simulation and verification. Compared to the single model prediction control method, the adaptive multi-model predictive control method can yield a much higher prediction accuracy, which can significantly eliminate the instability in the process of wind power generation.

Suggested Citation

  • Hongwei Li & Kaide Ren & Shuaibing Li & Haiying Dong, 2020. "Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System," Energies, MDPI, vol. 13(6), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1329-:d:331784
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    References listed on IDEAS

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    1. 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.
    2. Liu, Jizhen & Yao, Qi & Hu, Yang, 2019. "Model predictive control for load frequency of hybrid power system with wind power and thermal power," Energy, Elsevier, vol. 172(C), pages 555-565.
    3. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
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    Cited by:

    1. Lasantha Meegahapola & Siqi Bu, 2021. "Special Issue: “Wind Power Integration into Power Systems: Stability and Control Aspects”," Energies, MDPI, vol. 14(12), pages 1-4, June.
    2. Sergio Toledo & Edgar Maqueda & Marco Rivera & Raúl Gregor & Pat Wheeler & Carlos Romero, 2020. "Improved Predictive Control in Multi-Modular Matrix Converter for Six-Phase Generation Systems," Energies, MDPI, vol. 13(10), pages 1-13, May.

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