Fixed speed wind generator model parameter estimation using improved particle swarm optimization and system frequency disturbances

F. González-Longatt, P. Regulski, P. Wall, W. Terzija

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

8 Citations (Scopus)

Abstract

When planning power system operation it is important to have reliable models of the elements of the power system. Fixed speed wind turbines are a widely installed generation technology that use a single squirrel cage induction generator. The local wind profile and the properties of the induction machine constitute the main considerations when modeling these wind turbines. Existing methods for estimating the parameter values of induction machine models use a wide variety of parameter estimation algorithms but primarily use active and reactive power measurements made during start-up or direct mechanical testing to fit the model to. Proposed here is a parameter estimation method that applies improved particle swarm optimization to active and reactive power measurements made during a deviation in system frequency to estimate the parameter values of a induction machine model. This method has shown good accuracy and the use of on-line data may prove beneficial in future applications.

Original languageEnglish
Title of host publicationIET Conference on Renewable Power Generation, RPG 2011
Pages161
Number of pages1
Edition579 CP
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventIET Conference on Renewable Power Generation, RPG 2011 - Edinburgh, United Kingdom
Duration: 9 May 20118 Sep 2011

Publication series

NameIET Conference Publications
Number579 CP
Volume2011

Conference

ConferenceIET Conference on Renewable Power Generation, RPG 2011
Country/TerritoryUnited Kingdom
CityEdinburgh
Period9/05/118/09/11

Keywords

  • Generator modelling
  • Parameter estimation
  • Particle swarm optimization
  • Wind power
  • Wind turbine

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