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Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

Author

Listed:
  • Cherif, Hakima
  • Benakcha, Abdelhamid
  • Laib, Ismail
  • Chehaidia, Seif Eddine
  • Menacer, Arezky
  • Soudan, Bassel
  • Olabi, A.G.

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.

Suggested Citation

  • Cherif, Hakima & Benakcha, Abdelhamid & Laib, Ismail & Chehaidia, Seif Eddine & Menacer, Arezky & Soudan, Bassel & Olabi, A.G., 2020. "Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor," Energy, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220317928
    DOI: 10.1016/j.energy.2020.118684
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    References listed on IDEAS

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    3. Bouzid, M. & Champenois, G., 2013. "An efficient, simplified multiple-coupled circuit model of the induction motor aimed to simulate different types of stator faults," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 90(C), pages 98-115.
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    5. Besma Bessam & Arezki Menacer & Mohamed Boumehraz & Hakima Cherif, 2017. "Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 478-488, January.
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    Cited by:

    1. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    2. Jie Ma & Yingxue Li & Liying Wang & Jisheng Hu & Hua Li & Jiyou Fei & Lin Li & Geng Zhao, 2023. "Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest," Energies, MDPI, vol. 16(13), pages 1-17, June.
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. A. G. Olabi & Tabbi Wilberforce & Khaled Elsaid & Tareq Salameh & Enas Taha Sayed & Khaled Saleh Husain & Mohammad Ali Abdelkareem, 2021. "Selection Guidelines for Wind Energy Technologies," Energies, MDPI, vol. 14(11), pages 1-34, June.
    5. Seif Eddine Chehaidia & Hakima Cherif & Musfer Alraddadi & Mohamed Ibrahim Mosaad & Abdelaziz Mahmoud Bouchelaghem, 2022. "Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(18), pages 1-22, September.
    6. Noman Shabbir & Lauri Kütt & Bilal Asad & Muhammad Jawad & Muhammad Naveed Iqbal & Kamran Daniel, 2021. "Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study," Energies, MDPI, vol. 14(7), pages 1-16, April.

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