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Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

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  • Qinwei Wang

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Zeli Du

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Wenting Chen

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Chao Ai

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Xiangdong Kong

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Jiarui Zhang

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
    Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China)

  • Keyi Liu

    (College of Mechanical and Electrical Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China)

  • Gexin Chen

    (College of Mechanical and Electrical Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China)

Abstract

A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

Suggested Citation

  • Qinwei Wang & Zeli Du & Wenting Chen & Chao Ai & Xiangdong Kong & Jiarui Zhang & Keyi Liu & Gexin Chen, 2024. "Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network," Energies, MDPI, vol. 17(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:449-:d:1320666
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    References listed on IDEAS

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    1. Qin, Chao & Saunders, Gordon & Loth, Eric, 2017. "Offshore wind energy storage concept for cost-of-rated-power savings," Applied Energy, Elsevier, vol. 201(C), pages 148-157.
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