Recurrent Functional-Link-Based Fuzzy-Neural-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization
Faa-Jeng Lin
Li-Tao Teng
Jeng-Wen Lin
Syuan-Yi Chen
Dept. of Electr. Eng., Nat. Central Univ., Chungli;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: May 2009
Volume: 56,
Issue: 5
On page(s): 1557-1577
ISSN: 0278-0046
INSPEC Accession Number: 10601688
Digital Object Identifier: 10.1109/TIE.2008.2010105
First Published: 2008-12-02
Current Version Published: 2009-04-28
Abstract
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.
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