Intelligent control of doubly-fed induction generator systems using PIDNNs

Faa Jeng Lin, Jonq Chin Hwang, Kuang Hsiung Tan, Zong Han Lu, Yung Ruei Chang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

An intelligent control for a stand-alone doubly-fed induction generator (DFIG) system using a proportional-integral-derivative neural network (PIDNN) 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 field-oriented control to produce 3-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, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation.

Original languageEnglish
Pages (from-to)768-783
Number of pages16
JournalAsian Journal of Control
Volume14
Issue number3
DOIs
StatePublished - May 2012

Keywords

  • Doubly-fed induction generator
  • field-oriented control
  • proportional- integral-derivative neural network

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