Improved Parameter Estimation Techniques for Induction Motors using Hybrid Algorithms

TL;DR

This paper compares traditional algorithms and introduces a new hybrid method for estimating induction motor parameters, demonstrating superior convergence and accuracy through extensive simulations.

Contribution

A novel hybrid algorithm combining descent and natural optimization techniques for improved parameter estimation in induction motors.

Findings

Hybrid algorithm outperforms conventional methods in convergence speed.

Hybrid method achieves lower squared error rates.

Validated on large datasets of IEC and NEMA motors.

Abstract

The performance of Newton-Raphson, Levenberg-Marquardt, Damped Newton-Raphson and genetic algorithms are investigated for the estimation of induction motor equivalent circuit parameters from commonly available manufacturer data. A new hybrid algorithm is then proposed that combines the advantages of both descent and natural optimisation algorithms. Through computer simulation, the hybrid algorithm is shown to significantly outperform the conventional algorithms in terms of convergence and squared error rates. All of the algorithms are tested on a large data set of 6,380 IEC (50Hz) and NEMA (60Hz) motors.