Determining the operational status of a three phase induction motor using a predictive data mining model
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ResearchGate
Abstract
Description
The operational performance of a three-phase induction motor is impaired by
unbalanced voltage supply due to the generation of negative sequence
currents, and negative sequence torque which increase motor losses and also
trigger torque pulsations. In this study, data mining approach was applied in
developing a predictive model using the historical, simulated operational data
of a motor for classifying sample motor data under the appropriate type of
voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to
5%). A dataset containing the values of a three-phase induction motor’s
performance parameter values was analysed using KNIME (Konstanz
Information Miner) analytics platform. Three predictive models; the Naïve
Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors
were deployed for comparative analysis. The dataset was divided into two;
70% for model training and learning, and 30% for performance evaluation.
The three predictors had accuracies of 98.649%, 100% and 98.649%
respectively, and this confirms the suitability of data mining methods for
predictive evaluation of a three-phase induction motor’s performance using
machine learning
Keywords
TK Electrical engineering. Electronics Nuclear engineering