Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

• Published in: IEEE transactions on dielectrics and electrical insulation Vol. 27; no. 6; pp. 1861 - 1888
• Main Authors: Lu, Shibo, Chai, Hua, Sahoo, Animesh, Phung, B. T.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Condition monitoring; deep learning; Discharge; Focusing; Industries; intelligent fault diagnostics; Machine learning; Machine learning algorithms; partial discharge; Partial discharges; Pattern recognition; Real-time systems; State-of-the-art reviews; Training
• ISSN: 1070-9878; 1558-4135
• DOI: 10.1109/TDEI.2020.009070

This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.