A power outage doesn’t come out of the blue. It creeps up on you. A connection gets just a little bit hotter. A transformer just a little bit weird. A tree branch just a little too close.
It’s nothing dramatic. Nothing to make you jump out of your seat and take care of right now on a Tuesday afternoon. But it piles up, and eventually the lights go dark and everyone is scrambling to understand what happened.
The grid, however, never stops chattering. Sensors, meters, relays, logs. Data by the second, 24/7/365. The harsh reality is that even the smartest, most seasoned human can’t process it all in real time.
This is where AI comes in. Not as a predictor, certainly not as an omnipotent mastermind, but as an extremely rapid pattern detector. It recognizes when today’s data is starting to read like the first few pages of yesterday’s disaster scenarios.
Whenever someone uses the phrase “AI-powered” you should try to pause and prod a little more. What is it observing? What does it believe is going on? And once it has a belief, what changes because of it? If you don’t get good answers, you’re probably looking at marketing. If you get obvious but dull answers, you’re probably looking at something actually useful.
The reality of “AI for the grid”
I think AI has a marketing issue. It sounds like a black box is going to come along and make a bunch of decisions nobody will be able to explain, and maybe it will glow in the dark. That’s not happening on the power systems side.
AI is not supplanting engineers. It’s not in a control room somewhere making a final decision. Most of the time, AI is simply like a very diligent aide who says, “Well, here’s something you should pay attention to.”
Ultimately, AI digests massive amounts of data and identifies sequences of data that are likely precursors to failure. These sequences may occur over a long period of time (e.g., a transformer consistently exceeds a nominal temperature every month over a few months), or a very short time (e.g., a voltage signature that occurs seconds before a protection relay trips).
AI recognizes what is normal for a specific asset, feeder, or area and alerts an operator when there is a deviation.
And no, the outcome isn’t typically a “stop the presses!” moment. It’s something much more subtle. A risk score. A prioritized list. A simple, “We think these three feeders are vulnerable going into the storm,” or “this substation is more likely to fail than the others.”
The beauty of it is that it allows crews to cut through the noise and get to the meat of the issue. That reduces cost, but more importantly, it prevents a whole lot of unnecessary interruptions.
So, why do the lights go out in the first place?
Most outages are not the product of chaos. They are the product of routine. That daily strain on the equipment, and that one last push that got it to fail. In the USA and the UK, it is almost monotonous how predictable it is: weather, wear and time.
We blame the weather, because it’s the most overt threat. Storms blow down trees onto powerlines. Rain seeks out the flaws in your roof. Lightning bolts light up the sky.
A heatwave pinches from two sides, increasing load on the system while reducing the capacity of power lines. A cold snap inverts the situation, but keeps the squeeze on, increasing heating loads while causing aging equipment to shudder.
And there’s aging. Most grid hardware is tank-like in nature, but even tanks are finite. Transformers and breakers and switches and connectors all slowly decay from heat, vibration, corrosion, and switch cycles.
When they fail, it’s not the day they start going bad. It’s months or years later, when one more hot afternoon or one more heavy load turns out to be the straw that breaks the camel’s back.
Sometimes, it isn’t about the hardware. Sometimes, it’s about orchestration. The fact that a minor issue wasn’t contained in a timely fashion. Due to an outdated switch plan.
Due to a lack of visibility at the edge of the network. In those cases, it’s not just about speed, but also about wisdom. That’s where monitoring and analytics come into play, to prevent small issues from becoming everyone’s problem.
Four ways AI reduces outages
AI doesn’t come in and “solve” incidents with a silver bullet. That’s a lovely dream, but it’s not how any of this works in practice. What AI does instead is humbly and practically assist teams in identifying issues sooner, distinguishing signal from noise, and speeding up when problems arise.
Consider it less a knight in shining armor and more a coworker who pays close attention to everything and never needs to sleep.
Predictive maintenance (perform maintenance on issues before they cause failures)
The problem is, there are very few grid components that suddenly snap from “operating perfectly” to “completely broken.” Instead, it weakly sizzles a little more than it used to. Or there’s a slightly different buzzing noise.
Or the voltage and current do something funny and questionable that can’t easily get attention because there’s a hundred other things to keep track of. Human beings can handle giant red flags waving in their faces. It’s the tiny red flags that we’re bad at dealing with.
This is what AI does well. It figures out the baseline for a specific transformer or breaker. Not an average, not a standard, but this device, here, now. It warns you when it starts to stray.
That extra time is valuable. It gives us time to schedule an inspection, a repair, or a replacement, rather than reacting once the customers have already called us. In my experience, this benefit alone justifies most AI deployments. You avoid a single ugly failure, you’ve paid for a lot of quiet warnings.
Shorter fault detection and location time, meaning smaller fault location areas
Sometimes stuff still breaks. Even with all the planning and caution in the world, sometimes stuff still breaks. In that case, the best way to win fast is not to make the fault not happen, but to contain it and prevent it from spreading like a bad rumor.
AI identifies patterns on the system that suggest that there may be a fault, and likely the location of the fault. Again, not infallibly, not always, but it does identify issues sooner. Instead of sending a few trucks out to search for a fault, trucks are sent to a more specific area.
Anyone who has ever had to chase faults in the rain understands the value of even a small head start. You don’t read news articles about shorter power outages, but it matters to those on the receiving end.
Improved load forecasting and prevention of overload conditions
Not all outages are the result of equipment failure. Sometimes the grid is simply stretched to the limit. It happens during heat waves, when big events are going on, or when there’s a change in behavior that causes a surge in demand. Some components get close to their max, but nothing breaks, sort of like a party where the noise level is getting too high.
AIs can help them predict when those events will happen, how high the demand will go, where the highest pressure points are, so that they have a bit of time to prepare, to adjust their switching, balance loads and dispatch their resources. This isn’t sexy, but it keeps the grid away from having to throw a breaker or shedding load. I like to think of this as AI being the voice of reason.
Risk-based weather and fuel targeting, starting in the highest risk areas
Crews, time and budgets are finite. Storms are not. When a storm is coming, you don’t ask, “Can we get to all of this?” You ask, “What can we get to first?”
AI is part of the solution. The model fuses weather predictions with historical information about outages, asset health, and land characteristics to identify problematic feeders or regions. It does the same with tree pruning.
Rather than cutting back all vegetation all the time, AI identifies high-risk areas, given tree cover and past issues. This does not eliminate outages. It does mean better choices, fewer major problems, and faster restoration when Mother Nature strikes.
The big picture
“AI isn’t a magical shield around the grid that prevents a tree branch from falling or lightning from being lightning. It can help prevent minor issues from accumulating without notice and cut the duration of outages when disruptions occur.”
The analogy I use is that AI makes the grid a little more alert, a little more decisive and a little more assured. And when it’s working, you don’t even really see it.
A few less unexpected outages. A little more optimal maintenance. A little faster identification of faults. A little more targeted storm response. Nothing dramatic. Just a little more vigilant. And it doesn’t need to go home and sleep at night.
