Linear Induction Motor Parameter Estimation Based on Gray Wolves Optimization Algorithm

The Grey Wolf Optimization Algorithm (GWOA) is used in this study to estimate the optimal parameters of the three-phase linear induction motor TPLIM. Nameplate data is used as the basis for parameter estimation. The difference between the estimated and actual parameters is used to calculate the objective function, which serves as the primary problem goal, and is used as a fitness function of the GWOA. Additionally, the computed data taken from GWOA is compared with three popular optimization techniques: particle swarm optimization (PSO), deferential evaluation (DE), and genetic algorism (GA). The outcomes demonstrate the effectiveness and potential of the suggested GWOA. The findings show that GWOA can accurately determine the appropriate TPLIM parameters, leading to correct TPLIM performance. This study is utilized to estimate the performance analysis of the TPLIM. Compared to other optimization techniques; the estimated parameters using GWOA achieve the maximum proximity to the actual parameters and the best concordance between the predicted and observed values.