Offline parameter estimation of induction motor using a meta heuristic algorithm

Ritwik Giri, Aritra Chowdhury, Arnob Ghosh, B. K. Panigrahi, Swagatam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


An offline parameter estimation problem of an induction motor using a well known, efficient yet simple meta heuristic algorithm DEGL (Differential Evolution with a neighborhood based mutation scheme) has been presented in this article. Two different induction motor models such as approximate and exact models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. Differential Evolution is not completely free from the problems of slow or premature convergence, that's why the idea of a much more efficient variant of DE comes. The variant of DE used for solving this problem utilize the concept of the neighborhood of each population member. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the Particle Swarm Optimization algorithm. From the simulation results it is evident that DEGL outperforms both the algorithms (GA and PSO) in the estimation of the parameters of the induction motor.

Original languageEnglish (US)
Title of host publicationSwarm, Evolutionary, and Memetic Computing - First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Proceedings
Number of pages8
StatePublished - 2010
Externally publishedYes
Event1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010 - Chennai, India
Duration: Dec 16 2010Dec 18 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6466 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Differential Evolution
  • Genetic Algorithms
  • Metaheuristics
  • Particle Swarm Optimization


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