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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

86 引文 斯高帕斯(Scopus)

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.

原文英語
頁(從 - 到)1557-1577
頁數21
期刊IEEE Transactions on Industrial Electronics
56
發行號5
DOIs
出版狀態已發佈 - 2009

ASJC Scopus subject areas

  • 控制與系統工程
  • 電氣與電子工程

指紋

深入研究「Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization」主題。共同形成了獨特的指紋。

引用此