Separation and Classification of Partial Discharge Sources in Substations

João Victor Jales Melo (), George Rossany Soares Lira, Edson Guedes Costa, Pablo Bezerra Vilar, Filipe Lucena Medeiros Andrade, Ana Cristina Freitas Marotti, Andre Irani Costa, Antonio Francisco Leite Neto and Almir Carlos dos Santos Júnior
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João Victor Jales Melo: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
George Rossany Soares Lira: Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Edson Guedes Costa: Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Pablo Bezerra Vilar: Electrical Engineering Department, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Filipe Lucena Medeiros Andrade: Electrical Engineering Department, Federal Institute of Paraíba, Patos 58700-000, Brazil
Ana Cristina Freitas Marotti: Eletrobras, Centrais Elétricas Brasileiras S.A., Rio de Janeiro 20040-002, Brazil
Andre Irani Costa: Eletrobras, Centrais Elétricas Brasileiras S.A., Rio de Janeiro 20040-002, Brazil
Antonio Francisco Leite Neto: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil
Almir Carlos dos Santos Júnior: Postgraduate Program in Electrical Engineering, Federal University of Campina Grande, Campina Grande 58428-830, Brazil

Energies, 2024, vol. 17, issue 15, 1-16

Abstract: This work proposes a methodology for noise removal, separation, and classification of partial discharges in electrical system assets. Partial discharge analysis is an essential method for fault detection and evaluation of the operational conditions of high-voltage equipment. However, it faces several limitations in field measurements due to interference from radio signals, television transmissions, WiFi, corona signals, and multiple sources of partial discharges. To address these challenges, we propose the development of a clustering model to identify partial discharge sources and a classification model to identify the types of discharges. New features extracted from pulses are introduced to model the clustering and classification of discharge sources. The methodology is tested in the laboratory with controlled partial discharge sources, and field tests are conducted in substations to assess its practical applicability. The results of laboratory tests achieved an accuracy of 85% in classifying discharge sources. Field tests were performed in a substation of the Eletrobras group, allowing the identification of at least three potentially defective current transformers.

Keywords: partial discharge; classification; feature extraction (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: 2024
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