Big storms happen. Life is unpredictable that way. But in many cases, extreme weather can be a bit more predictable. For instance, If you operate in Florida, you can expect hurricanes. If you operate in California, you can expect wildfires. If you operate in New York, you can expect ice storms.
If we learned anything from Texas, it’s that freak winter storms can occur just about anywhere. During the disastrous storm of February 2021, subfreezing temperatures across the state overwhelmed the state's electricity infrastructure, causing massive power outages. Nearly 70% of those served by the state’s main power grid lost power, with an average outage time of 42 hours. Some outages lasted for more than two weeks.
Unfortunately, climate experts are saying that’s not all you can expect. Regardless of where you operate, more extreme weather is predicted to come your way. This rise of extreme weather has made one thing clear for energy executives: If dealing with mother nature isn’t top of mind, it should be.
Of course, many factors contributed to this catastrophic power failure. But it’s clear that better preparedness on behalf of the electrical companies could have limited the damage, which resulted in at least 111 people losing their lives.
Winterizing energy infrastructure, for example, can increase energy resilience. Readers might be inclined to wonder: how should I determine what equipment to winterize?
The combination of drones and advanced analytics is ideal for this use case. Should Texas utilities have performed pole hardening before the storm? Probably not. Should they have done a condition assessment of their poles before the storm? Yes. By seeing what’s at risk of failing, aging, old, non-compliant, etc. energy companies can see what parts are likely to fail in an ice storm and repair them now.
Double click on this concept and you’ll see another benefit for utilities. Machine learning, a form of AI, drastically speeds up the process of identifying potential failures to address. Whereas most utilities rely on analysts to manually review photos of assets—a process that can take several minutes per image—machine learning algorithms use information from tens of millions of images to instantly spot and surface potential issues for analysts to review. This saves time and improves accuracy.
In some cases, the emerging technology of Edge AI can further speed up this process. With Edge AI, all the computing power exists on the drone, giving energy companies real-time information on the condition of their assets. By moving analytics from the office to the field, energy companies can do things like automatically generate work orders and get materials into the field quickly so issues can be fixed in a few hours—not days.
Download the Preparing for Mother Nature ebook, to examine the three common extreme weather events and how better aerial intelligence can help you reduce damage and restore power quickly when disaster inevitably strikes.