Recurrent Functional-Link-Based Fuzzy-Neural-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization

被引：74

作者：

Lin, Faa-Jeng ^{[1
]}

Teng, Li-Tao ^{[2
]}

Lin, Jeng-Wen ^{[3
]}

Chen, Syuan-Yi ^{[1
]}

机构：

[1] Natl Cent Univ, Dept Elect Engn, Chungli 320, Taiwan

[2] Ind Technol Res Inst, MicroSyst Technol Ctr, Tainan 709, Taiwan

[3] Natl Dong Hwa Univ, Dept Elect Engn, Hualien 974, Taiwan

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2009年 / 56卷 / 05期

关键词：

STAND-ALONE;

D O I：

10.1109/TIE.2008.2010105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system. © 2009 IEEE.

引用

页码：1557 / 1577

页数：21

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