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Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine

Author

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  • Keighobadi, Jafar
  • Mohammadian KhalafAnsar, Hadi
  • Naseradinmousavi, Peiman

Abstract

To avoid the harmful effects of global warming on the earth planet and its atmosphere, the expansion of wind energy consumption based on new control techniques leads to improved energy capacity. The development of wind power plants on the sea is more efficient owing to the stronger wind flows in comparison with the onshore structure. Furthermore, stable installation of the offshore wind energy base meets the energy management requirements of today's consumers. As a result, dependency on fossil fuels as the main source of global warming decreases. The feedback control system and on-line sensor signals provide the stability and increased efficiency of the floating wind turbine. The aggressive environment and large size structure of the turbine lead to entering uncertainty in the turbine model and exogenous noises that should not be effectively compensated by conventional control methods. Therefore, a radial based functional neural network (RBFNN) controller is proposed to estimate and compensate the effect of uncertainty on feedback control of the wind turbine. For comparison purposes, a linear quadratic regulator (LQR) state feedback is also developed for optimal control of the floating wind turbines. The neural network adaptively determines the upper bound of uncertainty/noise. Therefore, conservative high-gain control actions to achieve robustness of the classical feedback controllers against structural deviations of the turbine body are decreased. In our newly proposed adaptive control algorithm, using a basic raised cosine function guides to restore the computational efficiency of RBFNN. With the Lyapunov-based stability analysis, the final limits of the closed-loop system and the convergence caused by the terminal sliding mode (TSM) tracking error are determined. Detailed software simulations of both the LQR and the designed RBFNN control systems indicate the superiority of the neural network-based approach.

Suggested Citation

  • Keighobadi, Jafar & Mohammadian KhalafAnsar, Hadi & Naseradinmousavi, Peiman, 2022. "Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922005104
    DOI: 10.1016/j.apenergy.2022.119132
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    References listed on IDEAS

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    1. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    2. Waseem Aslam Butt & Lin Yan & Kendrick Amezquita S., 2015. "Adaptive integral dynamic surface control of a hypersonic flight vehicle," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(10), pages 1717-1728, July.
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    Cited by:

    1. Zhou, Binzhen & Hu, Jianjian & Wang, Yu & Jin, Peng & Jing, Fengmei & Ning, Dezhi, 2023. "Coupled dynamic and power generation characteristics of a hybrid system consisting of a semi-submersible wind turbine and an array of heaving wave energy converters," Renewable Energy, Elsevier, vol. 214(C), pages 23-38.
    2. Duan, Guiyue & Gattari, Daniele & Porté-Agel, Fernando, 2025. "Theoretical and experimental study on power performance and wake characteristics of a floating wind turbine under pitch motion," Applied Energy, Elsevier, vol. 378(PA).
    3. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    4. Flavie Didier & Yong-Chao Liu & Salah Laghrouche & Daniel Depernet, 2024. "A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed," Energies, MDPI, vol. 17(10), pages 1-33, May.

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