UK first as SP Energy Networks uses AI to help tackle winter power cuts
With storm season in full force, SP Energy Networks is investigating the potential of using artificial intelligence to help predict faults in the electricity network ahead of severe weather hitting
In a UK first, SP Energy Networks is trialling AI technology to better pinpoint potential faults on the electricity network caused by severe weather and help ensure equipment and engineers are on hand to tackle problems – even before they happen.
The revolutionary £5 million Predict4Resilience project will use AI technology to predict where faults could occur up-to seven days in advance, allowing the electricity network operator to mobilise engineers and equipment to shorten the time power supplies could be disrupted.
By using AI, historic weather and fault data along with network asset and landscape information are used to develop machine learning models. Combined with real-time weather forecasting, Predict4Resilience will inform SP Energy Networks’ control room about where the weather will hit and what damage it’s expected with much more accurately than ever before. This will enable the control room to mobilise teams and send them out ahead of time, ready to restore power should a fault occur.
This innovative technology adds to SP Energy Networks’ storm response and will ultimately allow the network provider to respond more quickly to power cuts and more efficiently target resources ahead of storms.
Guy Jefferson, Chief Operating Officer at SP Energy Networks, said: “Ahead of a severe weather event we mobilise hundreds of engineers, vehicles, and generators alongside thousands of pieces of other materials so we are ready to restore power as quickly and as safely as possible.
“We know the disruption severe weather can bring to our customers and we are constantly investing in our network and investigating new technologies that could be used to keep this disruption to a minimum.
“Projects like Predict4Resilience offer us another tool to help inform our decision making during a storm and help to reduce the time it takes us to restore power, minimising the impact of severe weather on our customers and communities even further.
“Through collaboration with Scottish and Southern Electricity Networks Distribution to expand our testing area, the trial phase of this project will provide us with robust learnings to meet our ambition of rolling this technology out across the UK.”
The leading network provider is working with partners to roll out the technology across the UK, including The University of Glasgow, who are developing the AI methods that underpin this new forecasting capability; Scottish and Southern Electricity Networks Distribution, which will use the findings to test a different regulatory area, resulting in a wider scale area being tested.
With storm season in full force, SP Energy Networks is investigating the potential of using artificial intelligence to help predict faults in the electricity network ahead of severe weather hitting
In a UK first, SP Energy Networks is trialling AI technology to better pinpoint potential faults on the electricity network caused by severe weather and help ensure equipment and engineers are on hand to tackle problems – even before they happen.
The revolutionary £5 million Predict4Resilience project will use AI technology to predict where faults could occur up-to seven days in advance, allowing the electricity network operator to mobilise engineers and equipment to shorten the time power supplies could be disrupted.
By using AI, historic weather and fault data along with network asset and landscape information are used to develop machine learning models. Combined with real-time weather forecasting, Predict4Resilience will inform SP Energy Networks’ control room about where the weather will hit and what damage it’s expected with much more accurately than ever before. This will enable the control room to mobilise teams and send them out ahead of time, ready to restore power should a fault occur.
This innovative technology adds to SP Energy Networks’ storm response and will ultimately allow the network provider to respond more quickly to power cuts and more efficiently target resources ahead of storms.
Guy Jefferson, Chief Operating Officer at SP Energy Networks, said: “Ahead of a severe weather event we mobilise hundreds of engineers, vehicles, and generators alongside thousands of pieces of other materials so we are ready to restore power as quickly and as safely as possible.
“We know the disruption severe weather can bring to our customers and we are constantly investing in our network and investigating new technologies that could be used to keep this disruption to a minimum.
“Projects like Predict4Resilience offer us another tool to help inform our decision making during a storm and help to reduce the time it takes us to restore power, minimising the impact of severe weather on our customers and communities even further.
“Through collaboration with Scottish and Southern Electricity Networks Distribution to expand our testing area, the trial phase of this project will provide us with robust learnings to meet our ambition of rolling this technology out across the UK.”
The leading network provider is working with partners to roll out the technology across the UK, including The University of Glasgow, who are developing the AI methods that underpin this new forecasting capability; Scottish and Southern Electricity Networks Distribution, which will use the findings to test a different regulatory area, resulting in a wider scale area being tested.
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NewsTranscript
00:00 [Music]
00:19 Guy Jefferson, Chief Operating Officer, SP Energy Networks.
00:24 We're here today in our Cambuslang depot.
00:27 This is one of the locations in Scotland that we manage our emergency actions out of.
00:32 So here at our Cambuslang depot, this is one of the locations in central Scotland
00:37 that we utilise for our day-to-day working,
00:40 but also set up our emergency action centres in the event of storms.
00:43 So the Predict for Resilience project is a technology that we are going to utilise
00:48 to better affect our emergency response.
00:52 It uses AI and machine learning and data sets that we have from weather forecasts and previous storms.
00:59 We merge that together with the topography of the land that we have here in central Scotland
01:04 and also information like vegetation management, where that lies across the central belt.
01:11 We pull that together and when we see a weather forecast coming over the horizon
01:15 up to seven days in advance, we're better able to predict how that will impact our network
01:21 and therefore able to react more quickly in the event of a storm
01:25 and get people back on supply more quickly.
01:27 We are working with our partners on this project and using central Scotland and north Scotland as the pilot area.
01:34 But once we complete the work, we would look to roll that out across the UK
01:39 with all the other distribution network operators,
01:41 so it has a benefit across the UK customer base, not just Scotland.
01:46 So unfortunately, due to climate change, extreme weather is more likely.
01:52 Even though we have extreme weather quite a bit here in central Scotland in particular,
01:57 the likelihood of going off supply is still quite remote.
02:00 But if you do, it's really important that you phone 105, which is our emergency helpline,
02:05 and inform us that you're off supply.
02:07 Also really important, if you're out and about in difficult weather conditions,
02:12 you see one of our overhead lines on the ground perhaps, do not approach it.
02:17 Do not approach it. Phone us again on that same number, 105,
02:22 and we will mobilise engineers to come out and make safe
02:25 and hopefully get you back on supply as quickly as possible.
02:28 Adele Ramsden, lead engineer, Transmission Circuits Operations, Scottish Power.
02:33 This is one of our operational depots, and this is a storage and operational garage
02:38 that is used by Scottish Power Transmission Circuits team.
02:42 So within here, we've got all our equipment that we would need.
02:45 Our duty is when there's faults and storms and things go wrong,
02:49 we have to get out there and fix it to get our customers back on as soon as we can.
02:54 So we have this strategic store here where we've got all our equipment around us,
02:59 the spacers that go on the power lines, spacer chairs, all-terrain vehicles, spare conductor,
03:05 just basically anything we think we might need in the event of an emergency
03:09 to get that network back up and running.
03:11 I'm the lead engineer in Transmission Operations Circuits.
03:15 My background is very much construction-based, like building power lines in this type of industry.
03:21 I started this type of industry when I was about 17, so I've been here for 33 years now,
03:27 and I really enjoy what I do. It's every day is different.
03:30 You would come into work thinking that you're going to do something one day,
03:33 but it never turns out that way. There's always something else needs done.
03:36 In my early days, women in engineering became quite popular,
03:40 and then society swung away from that, and that kind of quietened down.
03:45 But then again, in the last decade, I've seen a big increase again.
03:49 Everybody's welcome here. Nobody would be picked because they were a woman or because they weren't a woman.
03:55 I think we're a very diverse company, and it genuinely is.
03:59 It tracks all walks of life, but I genuinely would say you get your job based on how well you can do your job.
04:05 We don't go down that route.
04:07 I won't say it's an easy life, because you've got to be prepared to do what everybody else does.
04:11 It doesn't matter what you are, you need to do that job as an engineer.
04:15 And if that means you're going out in the dark and the cold and bad weather and storms and snow,
04:20 working your social life, that's what you have to do.
04:23 You can't expect any allowances, but I would highly recommend it as a really good career.
04:27 [Music]
04:33 [MUSIC]