Complete Electrical Equivalent Circuit Based Modeling and Analysis of Permanent Magnet Direct Current (DC) Motors

Abstract

The modeling and analysis of permanent-magnet DC motor, whose attractiveness has increased with the availability of high-energy rare earth permanent magnet materials such as SmCo and NdFeB, is very important in recent years. The analysis of the established model is very important for researchers and engineers before siteworks. The most important features of the established model are that the equations are simple to obtain, the model can be set up using a simple platform, and ease of model analysis. Generally, for DC motor models, only the armature circuit model is given and only the equations of the mechanical system are given. In the armature circuit model, equations are obtained by switching to the s-domain, and analyzes are made on highly advanced platforms by using these equations. In this study, electrical equivalents of mechanical equations were obtained and both armature circuit and mechanical parameters of a DC motor were expressed as electrical circuit elements. Although the motor model includes mechanical parameters and variables, the whole model is expressed only with electrical elements and variables. Thus, a complete electrical equivalent circuit is proposed for the dynamic model of the permanent-magnet DC motor, in which both the armature and the mechanical part can be modeled as an electrical circuit. With the analyzes, the performance of the permanent magnet DC motor model was examined and it was seen that the system dynamics responses were compatible.

A Review on Background and Applications of Machine Learning in Materials Research

“…To achieve the right convergence of the fitting, Researchers must carefully create the equivalent circuit (EC) model, choose acceptable beginning values for the parameters of each component, and continually validate the output during the process. For this reason, Altay and Yildiz [18] recommended a ML-inverse approach that condensed a million separate fitting optimization issues into a solitary one. Using many perspectives from the ML literature resulted in an error rate of less than 1% for addressing a single optimization task.…”

“…To achieve the right convergence of the fitting, Researchers must carefully create the equivalent circuit (EC) model, choose acceptable beginning values for the parameters of each component, and continually validate the output during the process. For this reason, Altay and Yildiz[18] recommended a ML-inverse approach that condensed a million…”