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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Alashiri Olaitan
Covenant University
Control & Systems Engineering Electrical & Electronics Engineering

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Determining the operational status of a three phase induction motor using a predictive data mining model Aderibigbe Israel Adekitan; Adeyinka Adewale; Alashiri Olaitan
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v10.i1.pp93-103

Abstract

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
Co-Authors Aderibigbe Israel Adekitan Adeyinka Adewale