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PARTNERSHIP STUDY with Electricity North West on vibration monitoring of tap changers, using machine learning algorithms to see if a problem is developing. "We were producing up to 20GB of data per tap changer every month in trials," says Rodgers. "Machine learning can filter out what isn't meaning - ful and look only for what is useful." Samir Alilat, innovation and DSO strategist at Kelvatek, says the company had a lightbulb moment – almost literally – when it released the Bidoyng, or smart fuse. "As well as having a function on the network, as an auto-re - closer, we realised it could grab data as faults happened on the network," he says. "We knew that if we could get hold of the data behind the faults we could put it together with other information we held to work out the location of the fault for the network operator." This was useful in helping operators fix problems more quickly but Kelvatek has moved on to tackle the issue of predict - ing where things will go wrong. "We are interested in using or poor quality books then their conclusions will be coloured accordingly and it's the same for machines. "You need a lot of data at the correct quality and resolution. You also need a multi-discipli - nary team to analyse data and come up with the right models." Sandys calls for caution in how artificial intelligence is introduced to manage the networks. "Setting up shadow systems is important, where you run something using a machine learning algorithm at the same time as doing it in real time. Then you can work out whether the machine learning understands the system," she says. In the long-run though, she anticipates significant changes being driven by the new technology. "Ultimately National Grid should become a systems software company. The network could be run by machines. You need human intervention and oversight but fundamentally you should have some very effective tools by which things can be auto - mated." a network model to be able to advise network operators to deploy sensors so they can intervene or repair a fault before any incident happens. "Machine learning projects can help bring disparate data sources together to find relational patterns, creating powerful infor - mation that can be used to deliver benefits in a number of areas." Alilat cites a number of focuses for Kelvatek in how it uses machine learning to create value for operators and their customers. "You can look at cable and asset health; LCT detec - tion; load forecasting; voltage optimisation; simulating future scenarios for the market and the network. There are a lot of business cases for using collated data. "Artificial intelligence can establish patterns you might not see as a human. You are auto - mating the learning process." However there are some important elements to get right before the technology can do its job. "The quality and volume of data is important," says Alilat. "If a human reads incomplete About Kelvatek Kelvatek is the UK leader in low voltage fault detection and intelligent network automation, with a comprehensive, modular suite of software, managed services and connected smart devices under our SAPIENT predictive analytics and fault management platform. Kelvatek is an innovation partner supporting the transition for distribution system operators by offering smart integrated solutions that optimise network performance and efficiency today and tomorrow. Kelvatek is part of the Camlin Group, which operates in 20 countries. Camlin believes in high quality engineering and design and develops market leading products and services. It loves creating value for customers by solving difficult problems. For further information call 02476 320100 or email sales@kelvatek.com In association with: NETWORK / 30 / OCTOBER 2019