Control of doubly-fed induction generator system using PIDNNs

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

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

5 Scopus citations

Abstract

An intelligent control stand-alone doubly-fed induction generator (DFIG) system using 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 the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the stator 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 stator side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. Both the network structure and on-line learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages675-680
Number of pages6
DOIs
StatePublished - 2010
Event9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States
Duration: 12 Dec 201014 Dec 2010

Publication series

NameProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Conference

Conference9th International Conference on Machine Learning and Applications, ICMLA 2010
Country/TerritoryUnited States
CityWashington, DC
Period12/12/1014/12/10

Keywords

  • Doubly-fed induction generator
  • Field-oriented control
  • Proportional- integral-derivative neural network

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