Metaheuristic linear modeling technique for estimating the excitation current of a synchronous motor

Abstract

Abstract:The subject of modeling and estimating of synchronous motor (SM) parameters is a challenge mathematically.Although effective solutions have been developed for nonlinear systems in artificial intelligence (AI)-based models, problems are faced with the application of these models in power circuits in real-time. One of these problems is the delay time resulting from a complex calculation process and thus the difficulties faced in the design of real-time motor driving circuits. Another important problem regards the difficulty in the realization of a complex AI-based model in microprocessor-based real-time systems. In this study, a new hybrid technique is developed to solve the problems in AI-based nonlinear modeling approaches. Through this method, the relationships among the motor parameters can be described in a linear/quadratic SM form. The most effective and modern metaheuristic methods are utilized in the creation of SM forms. The SM forms developed in this study lead to an easy design and application of the SM driver software. Therefore, a model that is faster, more effective, and more easily applicable than AI-based popular methods is developed for SMs. The proposed techniques can also be applied to many other industrial modeling problems that have nonlinear features.

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

“…Several numerical results are presented which validate the performance of the suggested strategy. Finally, we conclude that comparing to the existing algorithms, such as GSA [14], ABC [14], and GA [14], the proposed SOS method in this paper is simpler, more robust and effective. Its superiority over the indicated evolutionary algorithms is demonstrated by the comparative analysis for the same model scheme with identical predictor variables.…”

“…In the study, mean response time comparisons with classical methods are also presented which verify that presented models have improved the response time compared to those using IKE and ANN. To the knowledge of this article's author, there Emre Çelik Estimation of synchronous motor excitation current using multiple linear regression model optimized by symbiotic organisms search algorithm may be a room for making better the estimation performance of the multiple regression model presented in [14]. In this sense, the values of the regression coefficients may even become more appropriate than the ones offered by GSA [14], ABC [14], and GA [14].…”

“…To the knowledge of this article's author, there Emre Çelik Estimation of synchronous motor excitation current using multiple linear regression model optimized by symbiotic organisms search algorithm may be a room for making better the estimation performance of the multiple regression model presented in [14]. In this sense, the values of the regression coefficients may even become more appropriate than the ones offered by GSA [14], ABC [14], and GA [14]. SOS algorithm that we use in this article, as the modern and powerful optimization algorithm, can solve this drawback by the ability to search for better regression coefficients, which may yield a decrease in estimation error value, and in turn contribute to the estimation performance.…”

“…In the present study, a multiple linear regression model similar to that reported in [14] is assumed with four predictor variables < , pf, , ∆ > to represent the SM excitation current . Since the estimation performance of this model is greatly affected by the regression coefficients, we formulated the estimation problem as constrained optimization problem and SOS algorithm is employed for the first time to find out regression coefficients better than the reported ones.…”

“…Afterwards, the load torque is increased, and then the entire process is repeated until reaching the nominal torque value of the concerned synchronous motor available in our laboratory. A more detailed description of the data collection procedure can be found in [11,14]. 70% of the collected experimental data is utilized as training data during the optimization of regression coefficients and the remaining portion is assumed as test data to check the prediction capability of the proposed model in presence of different values of SM predictor variables which are not presented to the model during optimization.…”