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

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

Energy, 2019, vol. 170, issue C, 40-52

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.

Keywords: Recurrent wavelet-based Elman neural network (RWENN); Offshore wind power; Ocean wave energy; Modified gravitational search algorithm (MGSA); Doubly fed induction generator (DFIG) (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:170:y:2019:i:c:p:40-52

DOI: 10.1016/j.energy.2018.12.084

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