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Determining the operational status of a three phase induction motor using a predictive data mining model

Adekitan, Aderibigbe I. and Adewale, Adeyinka A. and Olaitan, Alashiri (2019) Determining the operational status of a three phase induction motor using a predictive data mining model. International Journal of Power Electronics and Drive System, 10 (1). pp. 91-103. ISSN 2088-8694

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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

Item Type: Article
Uncontrolled Keywords: Machine learning Motor performance characteristics Negative and positive sequence component Power quality Three phase induction motor Voltage unbalance
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 13 Oct 2020 08:59
Last Modified: 13 Oct 2020 08:59

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