Squirrel-cage induction generator system using hybrid wavelet fuzzy neural network control for wind power applications

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/108276

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108276

Title:	Squirrel-cage induction generator system using hybrid wavelet fuzzy neural network control for wind power applications
Authors:	林法正;Lin, Faa-Jeng;Tan, Kuang-Hsiung;Fang, Dun-Yi
Contributors:	資訊電機學院電機工程學系
Keywords:	Artificial Intelligence;Computational Biology/Bioinformatics;Computational Science and Engineering;Computer Science;Data Mining and Knowledge Discovery;Image Processing and Computer Vision;Original Article;Probability and Statistics in Computer Science
Date:	2015-05-01
Issue Date:	2026-04-23 14:41:38 (UTC+8)
Publisher:	Springer London;London: Springer London
Abstract:	摘要： An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power applications using hybrid 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 to power grid. Moreover, the dynamic model of the SCIG system and an ideal computed torque controller are developed for the control of the square of DC-link voltage. Furthermore, an intelligent hybrid WFNN controller and two WFNN controllers, which are computation intensive approaches, are proposed for the AC/DC power converter and the DC/AC power inverter, respectively, to improve the transient and steady-state responses of the SCIG system at different operating conditions. In the intelligent hybrid WFNN controller, to relax the requirement of the lumped uncertainty in the design of the ideal computed torque controller, a WFNN is designed as an uncertainty observer to adapt the lumped uncertainty online. Finally, the feasibility and effectiveness of the SCIG system for grid-connected wind power applications are verified with experimental results. 其他題名： Neural Comput & Applic 出版者： London: Springer London 出版日期： 2015-05-01 出處： Neural computing & applications, 2015-05, Vol.26 (4), p.911-928 資源來源： EBSCOhost Academic Search Premier 版權： The Natural Computing Applications Forum 2014 識別號： ISSN: 0941-0643 識別號： EISSN: 1433-3058 識別號： DOI: 10.1007/s00521-014-1759-x
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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