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Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems

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  • Yin, Xiuxing
  • Jiang, Zhansi
  • Pan, Li

Abstract

An adaptive integral sliding mode controller is proposed to maximize wind power extraction by maintaining the optimum rotation speed of wind turbine. In the proposed controller, an integral sliding mode control law is designed to track the optimum turbine rotation speed based on a recurrent neural network (RNN) that is used to identify the uncertain wind turbine dynamics. An online update algorithm is then derived to update the weights of the RNN in real time and hence to facilitate the maximum power extraction control. The stability of the overall control system is guaranteed in the sense of Lyapunov stability theory. Comparative experimental results demonstrate that the proposed controller outperforms a conventional control method in tracking the optimum turbine rotation speed and extracting the maximum wind power despite system uncertainties and high nonlinearities.

Suggested Citation

  • Yin, Xiuxing & Jiang, Zhansi & Pan, Li, 2020. "Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems," Renewable Energy, Elsevier, vol. 145(C), pages 1149-1157.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1149-1157
    DOI: 10.1016/j.renene.2018.12.098
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    Citations

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    Cited by:

    1. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2021. "Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions," Energy, Elsevier, vol. 219(C).
    2. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    4. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.
    5. Anto Anbarasu Yesudhas & Young Hoon Joo & Seong Ryong Lee, 2022. "Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed," Energies, MDPI, vol. 15(9), pages 1-17, April.

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