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Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems

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  • Lu, Kai-Hung
  • Hong, Chih-Ming
  • Xu, Qiangqiang

Abstract

A new approach to rotational speed control structures based on an optimized intelligent recurrent wavelet-based Elman neural network (RWENN) controller used for the integration of offshore wind and wave energy conversion systems driven by a doubly fed induction generator. The nodes connecting the weights of the RWENN are trained online using a backpropagation method. A modified gravitational search algorithm (MGSA) is developed to adjust the learning rates and improve learning capability. The proposed control scheme has improved the real power regulation and dynamic performance of a combined wind and ocean wave energy scheme over a wide range of operating conditions. The performance of this control scheme is assessed by comparing it to a traditional proportional-integral based control scheme in a series of case studies representative of maximum power generation. Simulations are carried out using PSCAD/EMTDC software to verify the robustness of the power electronics converters and the efficiency of the proposed controller under steady state and transient conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:40-52
    DOI: 10.1016/j.energy.2018.12.084
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    References listed on IDEAS

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