Singapore Institute of Technology
Energies - Partial Discharge Diagnostics Data Cleaning and Feature Extraction.pdf (631.57 kB)
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Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

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journal contribution
posted on 2023-07-06, 09:03 authored by Cheng Lock, Donny SohCheng Lock, Donny Soh, Sivaneasan Bala KrishnanSivaneasan Bala Krishnan, Jacob AbrahamJacob Abraham, Kai Xian Lai, King Jet TsengKing Jet Tseng, Jimmy Yongyi Fu

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a sixmonth period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy.


Singapore Institute of Technology (SIT) and the SP Group


Journal/Conference/Book title

Energies - Special Issue New Trends in Condition Monitoring and Diagnostics of Power System Assets

Publication date



  • Published

Corresponding author

Sivaneasan Bala Krishnan