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Novel Intelligent Control Technology for Enhanced Stability Performance of an Ocean Wave Energy Conversion System

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  • 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, Quanzhou 362700, China)

  • Chih-Ming Hong

    (Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan)

  • Xiaojing Tan

    (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, Quanzhou 362700, China)

  • Fu-Sheng Cheng

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 83347, Taiwan)

Abstract

In this article, a novelty control structure of grid-connected doubly-fed induction generator (DFIG) based on a function link (FL)-based Wilcoxon radial basis function network (FLWRBFN) controller is proposed. The back-propagation (BP) method is used online to train the node connecting weights of the FLWRBFN. To improve the online learning capability of FLWBFN, differential evolution with particle swarm optimization (DEPSO) is used to tune the learning rates of FLWRBFN. For high randomness of wave energy generation, the transmission power between generators and electrical grids is easy to unstable and AC bus voltage and DC voltage will also lose constant under the conditions of variable generator speed and variable load. Therefore, the proposed intelligent controller can maintain the above power balance and voltage constant and reduce fluctuation. Finally, PSCAD/EMTDC software is used to simulate and study various cases to confirm the robustness and usefulness of the proposed intelligent control technology applied to an ocean wave energy conversion system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2027-:d:531118
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    References listed on IDEAS

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    1. Kai-Hung Lu & Chih-Ming Hong & Zhigang Han & Lei Yu, 2020. "New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems," Energies, MDPI, vol. 13(1), pages 1-21, January.
    2. Hong, Yue & Waters, Rafael & Boström, Cecilia & Eriksson, Mikael & Engström, Jens & Leijon, Mats, 2014. "Review on electrical control strategies for wave energy converting systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 329-342.
    3. Lin, Whei-Min & Hong, Chih-Ming, 2010. "Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system," Energy, Elsevier, vol. 35(6), pages 2440-2447.
    4. Ting-Chia Ou & Kai-Hung Lu & Chiou-Jye Huang, 2017. "Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)," Energies, MDPI, vol. 10(4), pages 1-16, April.
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    Cited by:

    1. 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.

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