Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor. (1st December 2020)

• Record Type: Journal Article
• Authors: Cherif, Hakima; Benakcha, Abdelhamid; Laib, Ismail; Chehaidia, Seif Eddine; Menacer, Arezky; Soudan, Bassel; Olabi, A.G.
• Is Part Of: Energy. Volume 212(2020)
• Publication Date: 2020-12-01
• Subjects: Induction motor -- Inter-turn short circuit -- Diagnosis.Discrete wavelet transform -- Discrete wavelet energy -- Discrete wavelet energy ratio -- Artificial neural networks; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118684
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes an improved diagnosis method for early detection and localization of Inter-Turn Short Circuit (ITSC) faults in the stator winding of the induction motor (IM). The main advantages of the method are the simplicity, low cost, and accurate diagnosis of these types of faults such that it can detect and localize even a low number of shorted turns faults in the stator winding of the motor. This is achieved by using a novel indicator that is based on the Discrete Wavelet Energy Ratio (DWER) of three stator currents, with Artificial Neural Network (ANN). Three different models of typical neural networks, namely, Multi-Layer perceptron (MLP), radial basis function (RBF), and Elman Neural Network (ENN) based on Bayesian Regularized (BR) training algorithm are proposed for ITSC classification based on fault feature extraction using discrete wavelet transform. To test the effectiveness of the proposed method, several experimental tests were carried out under different operating conditions of the IM, which contains the healthy and the ITSC faults cases that have experimented under various loads and different numbers of shorted turns in the three phases of the motor. The obtained results proved that the DWER is an accurate and robust indicator to diagnose the ITSC fault, this is confirmed by ANN results which gave the best results with the Bayesian regularized Elman network model that has the highest performance with minimal error rate is 10 −9 .Consequently, the combination DWER-ENN has assured its ability to accurately detect high and even low numbers of the shorted turns and localize the defective phase even within various loads in the IM. Highlights: DWER is an accurate and robust indicator to diagnose the ITSC fault. An improved Elman Neural Network (ENN) gave the best classification of the ITSC faults compared to MLP, and RBF networks. DWER-ENN is an efficient approach to early detection and localization of the ITSC fault under different conditions.