Santos Júnior, A.C.; Serres, A.J.R.; Xavier, G.V.R.; da Costa, E.G.; Serres, G.K.F.; Leite Neto, A.F.; Carvalho, I.F.; Nobrega, L.A.M.M.; Lazaridis, P. Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning. Electronics2024, 13, 2399.
Santos Júnior, A.C.; Serres, A.J.R.; Xavier, G.V.R.; da Costa, E.G.; Serres, G.K.F.; Leite Neto, A.F.; Carvalho, I.F.; Nobrega, L.A.M.M.; Lazaridis, P. Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning. Electronics 2024, 13, 2399.
Santos Júnior, A.C.; Serres, A.J.R.; Xavier, G.V.R.; da Costa, E.G.; Serres, G.K.F.; Leite Neto, A.F.; Carvalho, I.F.; Nobrega, L.A.M.M.; Lazaridis, P. Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning. Electronics2024, 13, 2399.
Santos Júnior, A.C.; Serres, A.J.R.; Xavier, G.V.R.; da Costa, E.G.; Serres, G.K.F.; Leite Neto, A.F.; Carvalho, I.F.; Nobrega, L.A.M.M.; Lazaridis, P. Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning. Electronics 2024, 13, 2399.
Abstract
This work presents a methodology for the generation and classification of phase resolved partial discharges (PRPD) patterns based on the use of a printed UHF monopole antenna and signal con-ditioning circuit to reduce hardware requirements. For this purpose, envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric disks with internal cavities in an oil cell, 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 reference. After acquisition of conditioned UHF signals, digital signal filtering threshold technique was used, and peaks of partial discharges envelopes 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 ma-chine and decision tree. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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