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Machine Learning Enhances Network Reliability at Singapore Substations

Singaporean researchers have developed a platform that automates and speeds up the detection of insulation failure in SP Group substations, helping to prevent blackouts. Since February 2021, the machine learning platform has helped to reduce 90% of the time spent daily on these routine checks. This eases the officers’ workload and enables them to focus on analysing the measurements suspected to have abnormalities.

As the owner and operator of Singapore’s electricity network, the company has more than 11,000 substations delivering electricity to industrial, commercial and residential consumers here. Part of the work in the 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.

To make the process more efficient, the researchers devised a machine learning platform that analyses the waveform data uploaded from the handheld devices, and flags 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.

Typically, going through five waveform data files would take about 10 minutes manually. The platform runs through at least 50 waveform data files in the same amount of time. That is a 10-fold efficiency increase. As a result, the number of suspected partial discharge cases that officers review daily has dropped significantly from around 400 to 40. The project took nine months to complete. The machine learning platform can be installed on the company’s local machines from the shared drives and is easily accessible off-site even without internet access.

Machine learning is critical to leave the lab and enter places where people work, and ultimately makes a positive difference in their lives. The team is happy to continue on this journey as Singapore develops its infrastructure to meet the next stage of growth. The team is very pleased with the good outcome of this project, which allowed them to improve their productivity. They will continue to explore the use of advanced technology in our pursuit of even better network reliability.

Singapore has been focusing on developing machine learning for a variety of purposes including combatting fake media. As reported by OpenGov Asia, AI Singapore launches the “Trusted Media Challenge”, a five-month-long competition aimed at attracting the Artificial Intelligence (AI) community to design and test out AI models and solutions that will easily detect audio-visual fake media, where both video and audio modalities may be modified.

The initiative – targeted at AI enthusiasts and researchers from around the world – aims to also strengthen Singapore’s position as a global AI hub by incentivising the involvement of international contributors, and sourcing innovation ideas globally.

Fake media technology or deepfake tech is becoming mainstream, delivering benefits and yet posing a variety of threats. The technology has allowed movie producers to manage videos and dialogues without expensive reshoots, facilitated professional training, or has been used to protect the identities of those being persecuted, among other applications.

At the other end of the spectrum, deepfakes are used to sow mistrust and seed scamming, making them an existential threat to societies today. If left unchecked, fake media risks becoming a serious national security concern.


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