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
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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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