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Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

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  • Lei Wang
  • Yongqiang Liu

Abstract

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

Suggested Citation

  • Lei Wang & Yongqiang Liu, 2018. "Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:1869232
    DOI: 10.1155/2018/1869232
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