Bacterial Foraging Technique Based Parameter Estimation of Induction Motor from Manufacturer Data

Induction machine parameters supplied by manufacturers are usually sufficient for short-circuit analysis only. System studies that involve transient simulation of machines require additional parameters that are not readily available but, nonetheless, are essential for an accurate modeling of the machines. This article presents a new stochastic optimization technique to estimate the equivalent circuit parameters of induction machines from the manufacturer data, such as nameplate data and motor performance characteristics, using the bacterial foraging technique. The equivalent circuit parameters are obtained as the solution for the error minimization function between the estimated and manufacturer data. The method has the advantage of not requiring any invasive measurements. The feasibility of the proposed bacterial foraging technique has been tested and examined on two different sample motors, and it has been benchmarked with particle swarm optimization, immune algorithm, and classical parameter estimation methods. The simulation results reveal that the proposed technique efficiently solved the parameter estimation problems and outperforms the particle swarm optimization and immune algorithm methods in terms of solution quality and convergence properties.