Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

Faa Jeng Lin, Kuang Hsiung Tan, Chia Hung Tsai

研究成果: 雜誌貢獻期刊論文同行評審

13 引文 斯高帕斯(Scopus)

摘要

An improved differential evolution (IDE) algorithm-based Elman neural network (ENN) controller is proposed to control a squirrel-cage induction generator (SCIG) system for grid-connected wind power applications. First, the control characteristics of a wind turbine emulator are introduced. Then, an AC/DC converter and a DC/AC inverter are developed to convert the electric power generated by a three-phase SCIG to the grid. Moreover, the dynamic model of the SCIG system is derived for the control of the square of DC-link voltage according to the principle of power balance. Furthermore, in order to improve the transient and steady-state responses of the square of DC-link voltage of the SCIG system, an IDE-based ENN controller is proposed for the control of the SCIG system. In addition, the network structure and the online learning algorithm of the ENN are described in detail. Additionally, according to the different wind speed variations, a lookup table built offline by the dynamic model of the SCIG system using the IDE is provided for the optimisation of the learning rates of ENN. Finally, to verify the control performance, some experimental results are provided to verify the feasibility and the effectiveness of the proposed SCIG system for grid-connected wind power applications.

原文???core.languages.en_GB???
頁(從 - 到)988-1001
頁數14
期刊IET Renewable Power Generation
10
發行號7
DOIs
出版狀態已出版 - 1 7月 2016

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