Logistic regression as a classifier in the diagnostic of electrical failures in a permanent magnet synchronous generator
DOI:
https://doi.org/10.46842/ipn.cien.v29n1a04Keywords:
binary classifier, logistic regression, faults, short circuit, stator, synchronous generatorAbstract
In this paper, a binary classifier used logistic regression to identify short circuit faults of the stator in a permanent magnet synchronous generator (PMSG). By processing the line currents of the PMSG, the data is obtained with which the feature extractor supplies information to the binary classifier. The feature extractor processes the currents through the Haar discrete wavelet transform, from which the practical values of its coefficients are obtained. It was joined with the objective function, the data received by the binary classifier in the training and validation process. Two metrics are analyzed to validate the performance of logistic regression in different fault conditions in the stator. Experimental tests are presented from a PMSG test bench in which the currents analyzed by the binary classifier for fault diagnosis are acquired and digitally processed. The Python program was used for the binary classifier's logistic regression algorithm
References
I. Boldea, “Electric generators and motors: An overview,” CES Trans. Electr. Mach. Syst., vol. 1, no. 1, pp. 3-14, 2020, doi: https://doi.org/10.23919/TEMS.2017.7911104
M. Rezamand, M. Kordestani, R. Carriveau, D. S. K. Ting, ME. Orchard, M. Saif, “Critical Wind Turbine Components Prognostics: A Comprehensive Review,” IEEE Trans. Instrum. Meas., vol. 69, no. 12, pp. 9306-9328, 2020, doi: https://doi.org/10.1109/TIM.2020.3030165
S. Choi, et al., “Fault diagnosis techniques for permanent magnet AC machine and drives-A review of current state of the art,” IEEE Trans. Transp. Electrif., vol. 4, no. 2, pp. 444-463, 2018, doi: https://doi.org/10.1109/TTE.2018.2819627
G. Helbing, M. Ritter, “Deep Learning for fault detection in wind turbines,” Renew. Sustain. Energy Rev., vol. 98, pp. 189-198, 2018, doi: https://doi.org/10.1016/j.rser.2018.09.012
H. Henao, et al., “Trends in fault diagnosis for electrical machines: A review of diagnostic techniques,” IEEE Ind. Electron. Mag., vol. 8, no. 2, pp. 31-42, 2014, doi: https://doi.org/10.1109/MIE.2013.2287651
D. Basak, A. Tiwari, S. P. Das, “Fault diagnosis and condition monitoring of electrical machines - A review,” Proc. IEEE Int. Conf. Ind. Technol., Mumbai, India, pp. 3061-3066, 2006, doi: https://doi.org/10.1109/ICIT.2006.372719
Z. T. Mei, G. J. Li, Z. Q. Zhu, R. Clark, A. Thomas, Z. Azar, “Scaling Effect on Inter-Turn Short-Circuit of PM Machines for Wind Power Application,” 2021 IEEE Int. Electr. Mach. Drives Conf. IEMDC 2021, Hartford, CT, USA, 2021, doi: https://doi.org/10.1109/IEMDC47953.2021.9449606
Z. Mei, G. J. Li, Z. Q. Zhu, R. Clark, A. Thomas, Z. Azar, “Scaling Effect On Inter-Turn Short-Circuit Fault of PM Machines for Wind Power Application,” IEEE Trans. Ind. Appl., vol. 59, no. 1, pp. 789-800, 2023, doi: https://doi.org/10.1109/TIA.2022.3211249
Z. Nie, N. Schofield, “Condition monitoring of a permanent magnet synchronous generator for a wind turbine application,” IET Conf. Publ., vol. 2016, no. CP684, pp. 1-6, 2016, doi: https://doi.org/10.1049/cp.2016.0233
S. Ferrari, G. Dilevrano, P. Ragazzo, P. Pescetto, G. Pellegrino, “Fast Determination of Transient Short-Circuit Current of PM Synchronous Machines Via Magnetostatic Flux Maps,” IEEE Trans. Ind. Appl., vol. 59, no. 1, pp. 4000-4009, 2023, doi: https://doi.org/10.1109/TIA.2023.3265952
A. G. Olabi, et al., “A review on failure modes of wind turbine components,” Energies, vol. 14, no. 17, 2021, doi: https://doi.org/10.3390/en14175241
D. Wei, K. Liu, W. Hu, X. Peng, Y. Chen, R. Ding, “Short-Time Adaline Based Fault Feature Extraction for Inter-Turn Short Circuit Diagnosis of PMSM via Residual Insulation Monitoring,” IEEE Trans. Ind. Electron., vol. 70, no. 3, pp. 3103-3114, 2023, doi: https://doi.org/10.1109/TIE.2022.3167164
C. Verde, S. Gentil, R. Morales-Menéndez, Monitoreo y Diagnóstico Automático de Fallas en Sistemas Dinámicos, México: Trillas, 2011.
S. H. Kia, H. Henao, G. A. Capolino, “Efficient digital signal processing techniques for induction machines fault diagnosis,” in 2013 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD), Paris, France, 2013, pp. 232-246. doi: https://doi.org/10.1109/WEMDCD.2013.6525183
W. Yang, P. J. Tavner, M. Wilkinson, “Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms,” IET Conf. Publ., no. 538 CP, pp. 6-10, York, UK, 2008, doi: https://doi.org/10.1049/cp:20080473
S. Halder, S. Bhat, C. Bhaumik, R. Rakshit, “Stator Inter- Turn Fault Diagnosis in Permanent Magnet Synchronous Motor,” Proc. 2020 IEEE 1st Int. Conf. Smart Technol. Power, Energy Control. STPEC, Nagpur, India, 2020, pp. 3-8, 2020, doi: https://doi.org/10.1109/STPEC49749.2020.9297677
M. H. Toliyat, et al., “Application of Pattern Recognition to Fault Diagnosis,” in Electric Machines Modeling, Condition Monitoring and Fault Diagnosis, USA: Taylor and Francis Group, 2015.
I. H. Kao, W. J. Wang, Y. H. Lai, J. W. Perng, “Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning,” IEEE Trans. Instrum. Meas., vol. 68, no. 2, pp. 310-324, 2019, doi: https://doi.org/10.1109/TIM.2018.2847800
P. Pietrzak, M. Wolkiewicz, T. Orlowska-Kowalska, “PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network,” IEEE Trans. Ind. Electron., vol. 70, no. 5, pp. 5192-5202, 2022, doi: https://doi.org/10.1109/TIE.2022.3189076
W. Lee, G. Choi, “A Comprehensive Review of Fault-Tolerant AC Machine Drive Topologies: Inverter, Control, and Electric Machine,” 2021 IEEE 13th Int. Symp. Diagnostics Electr. Mach. Power Electron. Drives, SDEMPED, Dallas, TX, USA, 2021, pp. 269-275, 2021, doi: https://doi.org/10.1109/SDEMPED51010.2021.9605560
A. Sengupta, “A First Course in Machine Learning by Simon Rogers and Mark Girolami,” International Statistical Review, vol. 82, no. 1. 2014. doi: https://doi.org/10.1111/insr.12051_21
V. M. Raschka, Python Machine Learning, 3rd ed., Birmingham UK: Packt Birmingham-Mumbai, 2019.
J. S. Walker, A primer on wavelets and their scientific applications, 2nd ed., UK: Taylor & Francis, 2008.
H. Cherif, A. Menacer, B. Bessam, R. Kechida, “Stator inter turns fault detection using discrete wavelet transform,” Proc. IEEE 10th Int. Symp. Diagnostics Electr. Mach. Power Electron. Drives, pp. 138-142, Guarda, Portugal, 2015, doi: https://doi.org/10.1109/DEMPED.2015.7303681
C. Verde, S. Gentil, R. Morales-Menéndez, Monitoreo y Diagnóstico Automático de Fallas en Sistemas Dinámicos, México: Trillas, 2013.
R. A. Ortiz Medina, “Diagnóstico de Fallas Eléctricas en Estator de Máquinas Eléctricas de Aerogeneradores,” Tesis, Doctortado, Ciencias de la Ingeniería, Instituto Tecnológico de Aguascalientes, 2021.
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Copyright (c) 2025 M.C. José Antonio Álvarez Salas, Dr. Ricardo Alvarez Salas, Dr. Francisco Javier Villalobos Piña, Dr. Mario Arturo González García, Dra. Amparo Rodríguez Cobos, Dr. J. Rafael Molina Contreras (Autor/a)
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