Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Almir Carlos dos Santos Júnior, Alexandre Jean René Serres, George Victor Rocha Xavier, Edson Guedes da Costa, Georgina Karla de Freitas Serres, Antonio Francisco Leite Neto, Itaiara Félix Carvalho, Luiz Augusto Medeiros Martins Nobrega, Pavlos Lazaridis

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

Original languageEnglish
Article number2399
Number of pages17
JournalElectronics (Switzerland)
Volume13
Issue number12
DOIs
Publication statusPublished - 19 Jun 2024

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