Partial Discharge Waveform Identifier
- Donny Soh
- Jacob Abraham
- Sivaneasan Bala Krishnan
- Tseng King Jet
- Industry Partner: Singapore Power
Project Start & End Date
Channel of Collaboration and/or Funding
Problem to Solve
- As the owner and operator of Singapore’s electricity network, SP Group has more than 11,000 substations delivering electricity to industrial, commercial and residential consumers here. Part of the work in maintenance of substations includes checking for partial discharge – an electrical discharge that does not completely bridge the space between two conducting electrodes – which is a key symptom of deteriorating electrical insulation. A fault due to insulation failure can lead to the tripping of protection equipment, which could trigger blackouts and even a fire.
Previously, checking for insulation degradation was highly labour-intensive. Operators used a handheld device to extract patterns and waveforms measured by the equipment at the substations. They then manually scanned through the pattern and waveform data to look for potential abnormalities.
Solution and Notable Contribution
- To make the process more efficient, SIT researchers devised a machine learning platform that analyses the waveform data uploaded from the handheld devices, and flags out suspected partial discharge. The platform uses a unique algorithm, called adaptive clustering, which removes noise of varying levels to accurately isolate partial discharge data points for analysis.
Notable Outcome(s) / Progress and Next Step / Potential of Wider Application
- Deployment at company
- 90% time savings
- Internal press release
Machine Learning Enhances Network Reliability at Substations