Intelligent-controlled doubly fed induction generator system using PFNN

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107596

Title:	Intelligent-controlled doubly fed induction generator system using PFNN
Authors:	林法正;Lin, Faa-Jeng;Huang, Yi-Sheng;Tan, Kuang-Hsiung;Lu, Zong-Han;Chang, Yung-Ruei
Contributors:	資訊電機學院電機工程學系
Keywords:	Applied sciences;Artificial Intelligence;Computational Biology/Bioinformatics;Computational Science and Engineering;Computer Science;Computer science;control theory;systems;Data Mining and Knowledge Discovery;Exact sciences and technology;Image Processing and Computer Vision;Learning and adaptive systems;Original Article;Probability and Statistics in Computer Science
Date:	2013-06-01
Issue Date:	2026-04-23 14:18:32 (UTC+8)
Publisher:	Springer London;London: Springer-Verlag
Abstract:	摘要： An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results. 其他題名： Neural Comput & Applic 出版者： London: Springer-Verlag 出版日期： 2013-06-01 出處： Neural computing & applications, 2013-06, Vol.22 (7-8), p.1695-1712 資源來源： EBSCOhost Academic Search Premier 版權： Springer-Verlag London Limited 2012 版權： 2014 INIST-CNRS 識別號： ISSN: 0941-0643 識別號： EISSN: 1433-3058 識別號： DOI: 10.1007/s00521-012-0965-7
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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