Partial Discharges Monitoring for Electric Machines Diagnosis: A Review

Jonathan dos Santos Cruz (), Fabiano Fruett, Renato da Rocha Lopes, Fabio Luiz Takaki, Claudia de Andrade Tambascia, Eduardo Rodrigues de Lima and Mateus Giesbrecht
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Jonathan dos Santos Cruz: School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil
Fabiano Fruett: School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil
Renato da Rocha Lopes: School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil
Fabio Luiz Takaki: Eldorado Research Institute, Campinas 13083-898, Brazil
Claudia de Andrade Tambascia: Eldorado Research Institute, Campinas 13083-898, Brazil
Eduardo Rodrigues de Lima: Eldorado Research Institute, Campinas 13083-898, Brazil
Mateus Giesbrecht: School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-970, Brazil

Energies, 2022, vol. 15, issue 21, 1-31

Abstract: Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

Keywords: partial discharge; rotating machine; monitoring; motor; generator; machine; machine learning; deep learning; PWM; inverter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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