Finding fault: bringing fresh intelligence to LV network management
More consumers and more connections. More solar panels, more heat pumps – and lots more EVs. The relentless drive to decarbonisation is making it harder for energy operators to analyse and respond to complex, unpredictable load patterns on their distribution networks. Keeping the lights on has never been more challenging. And with customers increasingly reliant on supply continuity, the threats of interruptions and consequent penalties are major preoccupations for every DNO.
LV fault location and management has been a largely reactive process for the best part of a century; and after all that time it’s still a far from perfect science. The first indicator of a problem in that tricky last mile can often be when a customer phones in to say their lights are flickering or they’ve lost power altogether. But even then the underlying cause – or causes – that have triggered an inbound customer report may be unclear. There could be a genuine fault event that’s caused a blown fuse on the substation. Equally, a circuit could be a temporarily overloaded when lots of customers happen to be drawing power at that moment. While these LV cables could easily be eighty years old or more, digging up the streets to check their condition clearly isn’t an option.
Traditionally LV distribution networks have been characterised by a single supply point. While a blown fuse means lost power for customers connected directly to that point, nobody else on the network is affected. More recently, we’ve seen the rise of meshed networks fed from multiple points. If a fuse blows, other circuits on the network can help take the strain. However there’s a downside to this approach. Faults on one circuit can propagate rapidly, leading to a significantly higher number of customers being directly affected.
UK wide, our devices have recorded in excess of one million real customer fault incidents and over 15 million hours of load monitoring data over the last few years. As the strain on DNOs’ ageing infrastructure increases, the number of faults is rising faster than ever.
A proactive approach to LV fault monitoring
With upwards of half a million LV substations in the UK, the cost of putting a discrete monitoring device on every circuit is clearly prohibitive. As the distribution landscape becomes ever more challenging, what’s therefore needed is a more intelligent mechanism for DNOs to anticipate and remediate faults before customers are aware that there’s a problem.
At Kelvatek we’re harnessing our deep understanding of LV network behaviours to create innovative new fault detection and management strategies. Drawing on the talents of software engineers, physicists, mathematicians and data science experts, we are using data from UK energy assets to formulate predictive models that help network operators do their day jobs more efficiently. Powerful Artificial Intelligence and Machine Learning algorithms can sift through voltage patterns, sensor signals and historical datasets from LV cables and other assets, uncovering the real story hidden in gigabytes of network data.
The main object of their interest is a huge historic data set of real-life fault behaviours, collected by tens of thousands of connected monitoring devices sitting on operator’s networks. This extensive library of fault information is augmented with dynamic load patterns and a wide range of data points from other sources. Sharing information with DNO partners gives us a detailed picture of cable routes and connectivity, health and history plus the number and location of smart meters and other third-party devices on the network – and of course data from our devices.
This granular, continually updated picture about the network infrastructure itself is further augmented by live and historic insights into other factors that subtly shape customer demand patterns. This can be everything from prevailing weather conditions to economic and social/demographic data about a neighbourhood and its inhabitants. As an example, an area that’s dominated by affluent households with solar panels installed and a nearby Tesla dealership gives some strong hints about expected customer demand patterns.
Actionable intelligence: a forensic picture of network health
Aggregating and analysing this uniquely valuable dataset allows the team to create a sophisticated virtual model of an operator’s network. In turn, this yields a forensic picture of that network’s current health and demand trends over time. Armed with this intelligence we can help operators anticipate future faults or problematic changes in load profiles and predict when these issues are likely to affect customer service. Our DNO partners can thus enjoy a significant operational advantage with the ability to allocate resources in good time – whether it’s fitting a fuse, deploying network flexibility measures or adopting traditional network reinforcement techniques.