Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Fang Duan, Rastko Zivanovic, Said Al-Sarawi, David Mba

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Inaccurate motor parameters can lead to an inefficient motor control. Although several motor estimation methods have been utilized to estimate motor parameters, it is still challenging to ensure a good level of confidence in the estimation. In this paper, we propose a novel offline induction motor parameter estimation method based on sparse grid optimization algorithm. The estimation is achieved by matching the response of machines mathematical model with recorded stator current and voltage signals. This approach is noninvasive as it uses external measurements, resulting in reduced system complexity and cost. A globally optimal point was found by sampling on the sparse grid, which was created using the hyperbolic cross points and additional heuristics. This has resulted in reducing the total number of search points, and provided the best match between the mathematical model and measurement data. The estimated motor parameters can be further refined by using any local search method. The experimental results indicate a very good agreement between estimated values and reference values.

Original languageEnglish
Article number7479570
Pages (from-to)1453-1461
Number of pages9
Issue number4
Publication statusPublished - Aug 2016
Externally publishedYes


  • Global optimization
  • hyperbolic cross point (HCP)
  • induction motor
  • parameter estimation
  • sparse grid


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