Adaptive control of a variable-speed variable-pitch wind turbine using RBF neural network

Hamidreza Jafarnejadsani, Jeff Pieper, Julian Ehlers

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

13 Scopus citations

Abstract

To be competitive economically, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output in varying wind speed. In this paper, an adaptive control based on Radial-Basis-Function (RBF) neural network (NN) is proposed for different operation modes of variable-speed variable-pitch (VSVP) wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds, and smooth transition between these two modes. The adaptive neural network control approximates the non-linear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine. Second, the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.

Original languageEnglish
Title of host publication2012 IEEE Electrical Power and Energy Conference, EPEC 2012
Pages216-222
Number of pages7
DOIs
StatePublished - 2012
Event2012 IEEE Electrical Power and Energy Conference, EPEC 2012 - London, ON, Canada
Duration: 10 Oct 201212 Oct 2012

Publication series

Name2012 IEEE Electrical Power and Energy Conference, EPEC 2012

Conference

Conference2012 IEEE Electrical Power and Energy Conference, EPEC 2012
Country/TerritoryCanada
CityLondon, ON
Period10/10/1212/10/12

Keywords

  • Adaptive control
  • Generator torque control
  • Pitch Control
  • RBF neural network
  • Transient mode of operation
  • Variable-speed variable-pitch wind turbine
  • Varying wind speed

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