Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power

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Title:	Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power
Authors:	林法正;Lin, Faa-Jeng;Tan, Kuang-Hsiung;Fang, Dun-Yi;Lee, Yih-Der
Contributors:	資訊電機學院電機工程學系
Keywords:	active power output;AC‐DC power converter;AC‐DC power convertors;Algorithms;asynchronous generators;backpropagation;backpropagation learning algorithm;constant‐frequency;constant‐voltage;DC‐AC power convertors;DC‐AC power inverter;DC‐link voltage controller;electric current control;Electric power;electric power generation;fuzzy neural nets;grid‐connected wind power application;indirect field‐oriented mechanism;Intelligent controlled three‐phase squirrel‐cage induction generator system;intelligent WFNN controller;machine vector control;neurocontrollers;phase control;power generation control;power grids;reactive power control;reactive power output;SCIG;tracking controller;variable‐frequency;variable‐voltage;wavelet fuzzy neural network;wavelet transforms;WFNN training;Wind power;wind power plants
Date:	2013-08-30
Issue Date:	2026-04-23 14:17:51 (UTC+8)
Publisher:	Institution of Engineering and Technology;Stevenage: The Institution of Engineering and Technology
Abstract:	摘要： An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system for wind power. 出版者： Stevenage: The Institution of Engineering and Technology 出版日期： 2013-09 出處： IET renewable power generation, 2013-09, Vol.7 (5), p.552-564 資源來源： Wiley Online Library Journals [OA] Open Access 版權： The Institution of Engineering and Technology 版權： Copyright The Institution of Engineering & Technology Sep 2013 識別號： ISSN: 1752-1416 識別號： ISSN: 1752-1424 識別號： EISSN: 1752-1424 識別號： DOI: 10.1049/iet-rpg.2012.0201
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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