Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines
Keywords:discrete wavelet transform, induction machine, three-phase model, multilayer perceptron neural network
Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.
Wu Libo, Zhao Zhengming, Liu Jianzheng. A Single-Stage Three-Phase Grid-Connected Photovoltaic System With Modified MPPT Method and Reactive Power Compensation. IEEE Transactions on Energy Conversion, 2007, vol. 22, no. 4, pp. 881-886. doi: https://doi.org/10.1109/TEC.2007.895461.
Sakhara S., Saad S., Nacib L. Diagnosis and detection of short circuit in asynchronous motor using three-phase model. International Journal of System Assurance Engineering and Management, 2017, vol. 8, no. 2, pp. 308-317. doi: https://doi.org/10.1007/s13198-016-0435-1.
Bessam B., Menacer A., Boumehraz M., Cherif H. 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, 2017, vol. 8, no. S1, pp. 478-488. doi: https://doi.org/10.1007/s13198-015-0400-4.
Nacib L., Saad S., Sakhara S. A Comparative Study of Various Methods of Gear Faults Diagnosis. Journal of Failure Analysis and Prevention, 2014, vol. 14, no. 5, pp. 645-656. doi: https://doi.org/10.1007/s11668-014-9860-0.
Talhaoui H., Menacer A., Kessal A., Kechida R. Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. ISA Transactions, 2014, vol. 53, no. 5, pp. 1639-1649. doi: https://doi.org/10.1016/j.isatra.2014.06.003.
Cherif H., Benakcha A., Khechekhouche A., Menacer A., Chehaidia S.E., Panchal H. Experimental diagnosis of inter-turns stator fault and unbalanced voltage supply in induction motor using MCSA and DWER. Periodicals of Engineering and Natural Sciences, 2020, vol. 8, no. 3, pp. 1202-1216. doi: https://doi.org/10.21533/pen.v8i3.1058.g607.
Xianrong Chang, Cocquempot V., Christophe C. A model of asynchronous machines for stator fault detection and isolation. IEEE Transactions on Industrial Electronics, 2003, vol. 50, no. 3, pp. 578-584. doi: https://doi.org/10.1109/TIE.2003.812471.
Filippetti F., Franceschini G., Tassoni C. Neural networks aided on-line diagnostics of induction motor rotor faults. Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting, 1993, pp. 316-323. doi: https://doi.org/10.1109/IAS.1993.298942.
Schoen R.R., Lin B.K., Habetler T.G., Schlag J.H., Farag S. An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Transactions on Industry Applications, 1995, vol. 31, no. 6, pp. 1280-1286. doi: https://doi.org/10.1109/28.475698.
Said M.S.N., Benbouzid M.E.H., Benchaib A. Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation. IEEE Transactions on Energy Conversion, 2000, vol. 15, no. 1, pp. 66-70. doi: https://doi.org/10.1109/60.849118.
Shutenko O., Ponomarenko S. Analysis of distribution laws of transformer oil indicators in 110-330 kV transformers. Electrical Engineering & Electromechanics, 2021, no. 5, pp. 46-56. doi: https://doi.org/10.20998/2074-272X.2021.5.07.
Paranchuk Y.S., Shabatura Y.V., Kuznyetsov O.O. Electromechanical guidance system based on a fuzzy proportional-plus-differential position controller. Electrical Engineering & Electromechanics, 2021, no. 3, pp. 25-31. doi: https://doi.org/10.20998/2074-272X.2021.3.04.
Belbachir N., Zellagui M., Settoul S., El-Bayeh C.Z., Bekkouche B. Simultaneous optimal integration of photovoltaic distributed generation and battery energy storage system in active distribution network using chaotic grey wolf optimization. Electrical Engineering & Electromechanics, 2021, no. 3, pp. 52-61. doi: https://doi.org/10.20998/2074-272X.2021.3.09.
Bengharbi A.A., Laribi S., Allaoui T., Mimouni A. Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 42-47. doi: https://doi.org/10.20998/2074-272X.2022.6.07.
Abid M., Laribi S., Larbi M., Allaoui T. Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique. Electrical Engineering & Electromechanics, 2022, no. 5, pp. 55-59. doi: https://doi.org/10.20998/2074-272X.2022.5.09.
Bouchaoui L., Hemsas K.E., Mellah H., Benlahneche S. Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study. Electrical Engineering & Electromechanics, 2021, no. 4, pp. 3-11. doi: https://doi.org/10.20998/2074-272X.2021.4.01.
Bessous N., Zouzou S.E., Bentrah W., Sbaa S., Sahraoui M. Diagnosis of bearing defects in induction motors using discrete wavelet transform. International Journal of System Assurance Engineering and Management, 2018, vol. 9, no. 2, pp. 335-343. doi: https://doi.org/10.1007/s13198-016-0459-6.
Kechida R., Menacer A., Talhaoui H. Approach Signal for Rotor Fault Detection in Induction Motors. Journal of Failure Analysis and Prevention, 2013, vol. 13, no. 3, pp. 346-352. doi: https://doi.org/10.1007/s11668-013-9681-6.
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