Prediction of the voltage status of a three-phase induction motor using data mining algorithms

Abstract

Data mining has found application in many research fields for predictive analysis. In engineering, data mining has been applied for equipment fault prediction by using the historical fault data of the equipment to train a data mining model for predicting future events. Power supply variations, and power quality issues affect the performance of a three-phase induction motor (TPIM). In this study, the operational performance data of a TPIM was deployed as a dataset for training a Konstanz Information Miner based model, for predicting the voltage status of the motor. The prevailing voltage status is classified into three, and these are: under voltage (2–10%), rated voltage, or over voltage (2–10%). For comparative analysis, the Tree Ensemble, Decision Tree, Random Forest and Support Vector Machine (SVM) were deployed for the voltage prediction. The result shows that the SVM had the highest prediction accuracy of 84.85%. This creates a platform for developing embedded systems that are trained using knowledge acquired from data mining for performance monitoring of induction motors.