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Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller

Author

Listed:
  • Zhaozhi Wang

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    Fujian Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou 362700, China)

  • Shemeng Wu

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China)

  • Kai-Hung Lu

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    Fujian Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou 362700, China)

Abstract

Presently, among the global ocean energy technologies, the most conventional one is the wave energy power generation device based on the oscillating water column (OWC) wave energy converter. Given the fluctuation and randomness of waves and the complexity of the current power grid, the dynamic response of grid connections must be considered. Furthermore, considering the characteristics of the wave energy converter, this paper proposed an adaptive intelligent controller (AIC) for the permanent magnet synchronous generator (PMSG) in an OWC. The proposed controller includes a grey predictor, a recurrent wavelet-based Elman neural network (RWENN), and an adaptive critical network (ACN) to improve the stability of OWC power generation. This scheme can increase the maximum power output and improve dynamic performance when a transient occurs under the operating conditions of random wave changes. The proposed AIC for the PMSG based on OWC has a faster response speed, a smaller overshoot, and better stability than the traditional PI controller. This further verifies the availability of the proposed control strategy.

Suggested Citation

  • Zhaozhi Wang & Shemeng Wu & Kai-Hung Lu, 2022. "Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller," Energies, MDPI, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:133-:d:1012353
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    References listed on IDEAS

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    1. Faa-Jeng Lin & Yi-Hung Liao & Jyun-Ru Lin & Wei-Ting Lin, 2021. "Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control," Energies, MDPI, vol. 14(2), pages 1-24, January.
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    3. Kai-Hung Lu & Chih-Ming Hong & Xiaojing Tan & Fu-Sheng Cheng, 2021. "Novel Intelligent Control Technology for Enhanced Stability Performance of an Ocean Wave Energy Conversion System," Energies, MDPI, vol. 14(7), pages 1-15, April.
    4. Lu, Kai-Hung & Hong, Chih-Ming & Xu, Qiangqiang, 2019. "Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems," Energy, Elsevier, vol. 170(C), pages 40-52.
    5. Xiaomei Wu & Songjun Jiang & Chun Sing Lai & Zhuoli Zhao & Loi Lei Lai, 2022. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network," Energies, MDPI, vol. 15(18), pages 1-16, September.
    6. Shaowu Li & Kunyi Chen & Qin Li & Qing Ai, 2022. "A Variable-Weather-Parameter MPPT Method Based on Equation Solution for Photovoltaic System with DC Bus," Energies, MDPI, vol. 15(18), pages 1-25, September.
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    Cited by:

    1. Stefanizzi, Michele & Camporeale, Sergio Mario & Torresi, Marco, 2023. "Experimental investigation of a Wells turbine under dynamic stall conditions for wave energy conversion," Renewable Energy, Elsevier, vol. 214(C), pages 369-382.
    2. Sunil Kumar Mishra & Amitkumar V. Jha & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong & Pongsiri Mungporn, 2023. "Ocean Wave Energy Control Using Aquila Optimization Technique," Energies, MDPI, vol. 16(11), pages 1-21, June.

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