Comparison and analysis of various machine learning algorithms in predicting the excitation current of constant speed AC motor

Abstract

With the wide application of motor in industry, transportation, home appliances and other fields, the performance requirements of motor are getting higher and higher, in which the excitation current of constant speed AC motor is one of its important performance indicators. Although the traditional method based on physical model can calculate the motor excitation current accurately, it needs a lot of physical parameters and experimental data, which is expensive and difficult to popularize. Therefore, the research of predicting the excitation current of constant speed AC motor based on machine learning algorithm has important practical significance. Divide the data set into the training set and the test set in a 7:3 ratio. Decision tree regression model, Random forest regression model, adaboost regression model, Gradient lifting tree regression model, ExtraTrees regression model, CatBoost regression model and K nearest are used respectively neighbor regression model was trained and the evaluation indexes MSE, RMSE, MAE, MAPE and R2of the model were calculated. According to the results of model evaluation parameters, the Gradient lifting tree model had the best prediction effect, and its MSE reached 0.002. The adaboost model has the second best performance, and its MSE reaches 0.055. The performance of CatBoost, Decision tree and CatBoost is average, while the performance of K nearest neighbor is the worst, with an MSE of 33.205. This paper compares five different models and analyzes the reasons why they perform well. In practical application, it is necessary to select the appropriate model according to the specific problem, and adjust and optimize it to achieve better performance.