Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN

Whei Min Lin, Chih Ming Hong, Ting Chia Ou, Tai Ming Chiu

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)

Abstract

This paper presents the design of a fuzzy sliding mode loss-minimization control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training radial basis function network (RBFN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RBFN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. A sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation is investigated to estimate the optimal bound of uncertainties.

Original languageEnglish
Pages (from-to)1244-1251
Number of pages8
JournalEnergy Conversion and Management
Volume52
Issue number2
DOIs
Publication statusPublished - 2011 Feb

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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