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Maximum wind power tracking based on cloud RBF neural network

Author

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  • Wu, Zhong-Qiang
  • Jia, Wen-Jing
  • Zhao, Li-Ru
  • Wu, Chang-Han

Abstract

Based on the mathematical model of Permanent magnet synchronous generator (PMSG), maximum wind power tracking control strategy without wind speed detection is analyzed and a controller based on cloud RBF neural network and approximate dynamic programming is designed to track the maximum wind power point. Optimal power-speed curve and vector control principles are used to control the electromagnetic torque by approximate dynamic programming controller to adjust the voltage of stator, so the speed of wind turbine can be operated at the optimal speed corresponding to the best power point. Cloud RBF neural network is adopted as the function approximation structure of approximate dynamic programming, and it has the advantage of the fuzziness and randomness of cloud model. Simulation results show that the method can solve the optimal control problem of complex nonlinear system such as wind generation and track the maximum wind power point accurately.

Suggested Citation

  • Wu, Zhong-Qiang & Jia, Wen-Jing & Zhao, Li-Ru & Wu, Chang-Han, 2016. "Maximum wind power tracking based on cloud RBF neural network," Renewable Energy, Elsevier, vol. 86(C), pages 466-472.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:466-472
    DOI: 10.1016/j.renene.2015.08.039
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    References listed on IDEAS

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    1. Xie, Kaigui & Billinton, Roy, 2011. "Energy and reliability benefits of wind energy conversion systems," Renewable Energy, Elsevier, vol. 36(7), pages 1983-1988.
    2. Belu, Radian & Koracin, Darko, 2009. "Wind characteristics and wind energy potential in western Nevada," Renewable Energy, Elsevier, vol. 34(10), pages 2246-2251.
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    Cited by:

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    2. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    3. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    4. Bizon, Nicu, 2018. "Optimal operation of fuel cell/wind turbine hybrid power system under turbulent wind and variable load," Applied Energy, Elsevier, vol. 212(C), pages 196-209.

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