Title: Power control of wind energy conversion system under multiple operating regimes with deep residual recurrent neural network: theory and experiment

Authors: Zhongli Shen; Yuguang Niu; Yi Zuo; Qiyue Xie; Zhishen Chen

Addresses: State Key Laboratory of Alternate electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; Energy and Power Engineering College, Changsha University of Science and Technology, Changsha, Hunan 410000, China; Hunan Province 2011 Collaborative Innovation Centre of Clean Energy and Smart Grid, China ' State Key Laboratory of Alternate electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206,China ' Energy and Power Engineering College, Changsha University of Science and Technology, Changsha, Hunan 410000, China; Hunan Province 2011 Collaborative Innovation Centre of Clean Energy and Smart Grid, China ' Energy and Power Engineering College, Changsha University of Science and Technology, Changsha, Hunan 410000, China; Hunan Province 2011 Collaborative Innovation Centre of Clean Energy and Smart Grid, China ' Energy and Power Engineering College, Changsha University of Science and Technology, Changsha, Hunan 410000, China; Hunan Province 2011 Collaborative Innovation Centre of Clean Energy and Smart Grid, China

Abstract: This paper makes a research for the speed control of wind turbine system under multiple operating regimes, which also studied the sleep residual recurrent neural network method in this work. We aim at designing deep residual recurrent neural network robust controllers, which guarantee the existence of the multiple regime system poles in some predefined zone and wind speed precise tracking. Moreover, the feedback gains which guarantee desired speed tracking performance are obtained by solving the Lyapunov stability functions. The results are applied to a directly driven wind energy conversion experiment systems and the numerical experiment, comparing with the existing results, shows the satisfactory performance of the proposed method.

Keywords: power control; wind turbine system; deep residual recurrent neural network; multiple operating regimes.

DOI: 10.1504/IJCSM.2019.102703

International Journal of Computing Science and Mathematics, 2019 Vol.10 No.4, pp.413 - 428

Received: 09 Oct 2017
Accepted: 05 Dec 2017

Published online: 01 Oct 2019 *

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