Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 7; p. 2226
• Main Authors: Carvalho, Itaiara Felix, da Costa, Edson Guedes, Nobrega, Luiz Augusto Medeiros Martins, Silva, Allan David da Costa
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2024
• Subjects: Algorithms; Analysis; Classification; classification of partial discharges; Electric transformers; Failure; Identification; Localization; Machine learning; Magnetic fields; Monitoring systems; partial discharge; Sensors; signal conditioning system; classification of partial discharges; signal conditioning system; partial discharge
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24072226

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.