Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Abstract

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 non-invasive 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 (HCPs) 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.

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