Predicting the performance of electrical machines using machine learning

Abstract

Electrical machines play an important role in our day-to-day life. Electric machines like DC motors and 3- phase induction motors are essential systems and widely used in domestic, industrial and transportation systems. In order to operate the machines optimally and efficiently, in real time operations, it is required to predict the performance parameters at various loaded conditions. With the advancements in the field of predictive modelling and analytics, several researchers have applied in the area of energy consumption prediction, fault prediction, weather prediction, power grid management and so on. In this paper, the machine learning techniques are demonstrated that may be used to examine the performance of electrical machinery by forecasting performance characteristics like speed and efficiency. To validate the performance of the predictive model, an experiment was conducted at the laboratory on dc motor and 3- phase induction motor to generate the required dataset to train the regression algorithms. The model evaluation metrics such MSE and the R2 value showed that the model efficiently predicted the performance of the electrical machines.