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Big data and ML specialist to help solve challenges in transition to green energy

Fundamentals has worked with Scottish and Southern Electricity Networks and UK Power Networks to proactively identify network faults before failure using preventative maintenance.

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The Synaps project gives the ability to predict and identify faults on a network
The Synaps project gives the ability to predict and identify faults on a network

Electrical grid technology company Fundamentals has completed its acquisition of Powerline Technologies to leverage its big data and machine learning (ML) solutions and help network operators meet future electrification demands.

 

Fundamentals reports its (artificial intelligence) AI-powered asset management software identifies and accurately locates faults before they happen. It analyses the waveforms of electricity voltage and identifies an energy fault signature on any slightly unusual event in the underground cable.

 

Reliable network

 

As distribution network operators (DNOs) experience significant costs and customer outages due to faults on underground cables, they are looking for ways to run an even more reliable network at a lower cost, according to Fundamentals. Its solution means DNOs can predict and prevent faults before they become visible and are reported by customers.

 

Fundamentals has worked with Scottish and Southern Electricity Networks (SSEN) and UK Power Networks to proactively identify network faults before failure using preventative maintenance.

 

This proof-of-concept ‘Synaps’ project was backed by Ofgem’s innovation funding and is one of few Network Innovation Allowance (NIA) projects to be taken through to a Phase 2 project, Fundamentals claims. The project enabled engineers to use a library of big data drawn from networks, in conjunction with AI, machine learning and deep learning, to predict and locate faults on network infrastructure before they cause device or fuse failures.

“The future of the electricity network will benefit from the application of machine learning techniques to improve power system fault diagnostics, protection and automation as we transition to net-zero”

Data collected in the Synaps project including current, voltage and fault waveforms has been shared with all DNOs.

 

“The future of the electricity network will benefit from the application of machine learning techniques to improve power system fault diagnostics, protection and automation as we transition to net-zero,” said Stewart Reid, head of future networks at SSEN.

 

“The Synaps project gives us the ability to predict and identify faults on the network before they occur and helps us improve the service we provide to our customers.”

 

The £680,000, 14-month project was jointly run by SSEN, UK Power Networks and Fundamentals. The AI platform will continue to increase knowledge and ‘learn’ to recognise conditions that can cause a fault on the electricity network. Fundamentals said this insight is crucial as DNOs manage the demand of new electric vehicles, with more than 11 million electric vehicles expected to be on British roads by 2030.

 

“There has long been talk of how big data and AI can benefit electricity networks, but this is an example of it being put to a really practical use, in a way that’s going to benefit our customers by helping us to run an even more reliable network at lower cost,” added Ian Cameron, head of customer services and innovation at UK Power Networks.

 

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