Jacob Abraham
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jacob Abraham has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Donny Soh
ICT Cluster, Singapore Institute of Technology, Singapore 828608, Singapore
1 shared publication - Jimmy Fu Yongyi
SP Group, 2 Kallang Sector, Singapore 349277, Singapore
1 shared publication - Kai Xian Lai
SP Group, 2 Kallang Sector, Singapore 349277, Singapore
1 shared publication - King Jet Tseng
Singapore Institute of Technology, Singapore
1 shared publication - Sivaneasan Bala Krishnan
Singapore Institute of Technology
1 shared publication
Latest Publications
- Open Access
Partial Discharge Diagnostics: Data Cleaning and Feature Extraction
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 six-month 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.
Open AccessPartial Discharge Diagnostics: Data Cleaning and Feature Extraction
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 six-month 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.