Squirrel-cage induction generator system using intelligent control for wind power applications

Faa Jeng Lin, Kuang Hsiung Tan, Dun Yi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected power application using 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 from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
Duration: 7 Jul 201310 Jul 2013

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Country/TerritoryIndia
CityHyderabad
Period7/07/1310/07/13

Keywords

  • Power converter
  • Three-phase induction generator
  • Wavelet fuzzy neural network

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