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

Faa Jeng Lin, Kuang Hsiung Tan, Dun Yi Fang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)911-928
Number of pages18
JournalNeural Computing and Applications
Volume26
Issue number4
DOIs
StatePublished - 1 May 2015

Keywords

  • Indirect field-oriented mechanism
  • Intelligent hybrid control
  • Projection algorithm
  • Squirrel-cage induction generator
  • Wavelet fuzzy neural network

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