Estimation of Synchronous Motor Excitation Current Using Multiple Linear Regression Model Optimized by Symbiotic Organisms Search Algorithm

Abstract

In this paper, an effective and simple means of estimating the excitation current of a synchronous motor (SM) is presented for power factor correction task. First, a multiple linear regression model with four predictor variables such as motor load current, actual power factor, power factor error and excitation current change is formed to estimate the SM excitation current. Then, recently introduced symbiotic organisms search (SOS) algorithm is benefitted in the hope of searching better values of regression coefficients in that model using the data collected from the prepared experimental setup. The supremacy of SOS over some recently published algorithms such as genetic algorithm, artificial bee colony and gravitational search algorithm is widely attested through comparative computer simulations for the similar compensation system. The results exhibited in this article show that the presented technique outperforms the other reported popular algorithms from the aspects of simplicity, robustness and accuracy. In view of this, the suggested tuning of regression coefficients of the multiple linear regression model yields a better estimating performance of SM excitation current than the earlier studies.

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

“…It relies on observable data similarity and near-distance metrics for accurate prediction generation [38,54]. When modelling k-NN based on research in [35,54,61], the parameters shown in Table 5 were varied.…”

“…Regarding synchronous motors with electromagnets, the authors of [35] indicate the complexity and nonlinearity of the SM parameters. By applying the symbiotic organisms search (SOS) algorithm, gravitational search algorithm (GSA), ABC, and GA, the authors investigate the possibility of obtaining a high-quality algorithm with a small error of approximation, whereby the best results are achieved by SOS with a maximum error of 0.1703 A.…”