How AI Can Predict and Prevent Power Grid Failures

The increasing accessibility of artificial intelligence (AI) has caused many utility company decision-makers to explore how this technology could enhance grid stability. How can they apply this technology to optimize results?

Enable Autonomous Repairs

Severe or widespread power outages can strain the labor force, causing customers to wait days or weeks for electricity restoration. Besides being inconvenient at the best of times, prolonged power outages can be life-threatening to customers who rely on specialty medical equipment or cannot tolerate non-temperature-controlled environments.

A recent example of how power outages can swamp locally available technicians came in January 2025, when one of the strongest-recorded storms hit Ireland. Record-setting winds accompanied the weather event, resulting in many downed power lines and trees. Personnel arrived from several other European countries to assist with the repair efforts. Could AI support help someday, too?

Researchers in the United States hope so. They envision using AI to create a self-healing grid to detect and fix problems without human intervention. It would also automatically reroute electricity to prevent power outages.

The team ran various scenarios in a controlled environment to assess their artificial intelligence solution under development. Much of their work uses graph machine learning, which describes an electrical network’s topology, including a grid’s various interdependencies and the positions of the components.

If power line faults block the electricity flow, the system reconfigures itself with switches and gets power from other nearby, nondisrupted grid components. The AI does this in only milliseconds, making it highly efficient. Although the group’s first aim is to utilize their self-healing grid technology to prevent outages, they will next work on making it fix problem areas. Suppose this approach can address minor grid faults without human oversight. Then, technicians could tackle the more severe problems.

Predict Infrastructural Problems

AI applications in the energy sector have various pros and cons to consider. One of the much-discussed downsides is that artificial intelligence is extremely energy-intensive, so its rapidly increasing use has strained aging electric grids. The fact that technology has also become more popular while people are already using more electricity to charge their cars does not help.

Conversely, artificial intelligence can relieve some of the burden by making dynamic electricity distribution tweaks and balancing the excessive loads caused by demand peaks. Additionally, decision-makers in industries like manufacturing use it to analyze which processes use the most energy. Such insights can help companies gradually reduce their electricity bills.

Researchers also believe AI could help utility company leaders learn which infrastructural components are most at risk of damage from natural disasters. One group has focused on steel transmission towers and how back-to-back harsh weather events could make them less resilient.

Machine learning showed them that earthquake-hurricane combinations were particularly damaging, especially if the first disaster caused damage that people did not have time to repair before the next one struck. However, the order also matters, with infrastructure collapses being more likely if an earthquake happens first.

The group created fragility models, which could reveal to technicians the components of a large, geographically dispersed network that need the most urgent repairs to prevent disaster-related catastrophes. One useful thing they learned is that multihazard events cause different failure patterns. Additionally, many issues occur in the structure’s leg elements. That information could show repair personnel where to look for problems first.

Improve Maintenance Schedules

Performing regular checks on power grid assets, such as transformers, can increase uptime while reducing costly, unexpected repairs. Technicians should handle specific tasks at intervals ranging from daily to yearly. Many utility companies use project management software to track when inspections or maintenance occur. That is a good start, but some researchers see additional opportunities to apply AI.

One group has developed prognostic models that receive condition-monitoring data and estimate the remaining useful life of specific grid components. Then, decision-makers neither wait for equipment to fail nor schedule maintenance before it is necessary. Instead, they can schedule technician callouts at precisely the right times. That individualized approach saves time and costs, mainly because it can indicate which components need replacement or repair.

Real-world tests of this solution on solar inverters showed it could decrease unnecessary crew calls by up to 66% while slashing total maintenance costs by as much as 56%. The researchers’ data also showed that utilities have used approximately 70% of the largest and most expensive transformers for at least 25 years. An AI-based approach to asset management could show energy industry leaders which equipment to replace first.

Prepare the Grid for the Future

Many people do not realize how dependent they are on electricity until outages happen. However, increased power usage and more severe storms will likely increase the instances when they temporarily cannot benefit from its convenience. Thanks to artificial intelligence applications like those mentioned above, well-trained algorithms and predictive capabilities can ensure electricity grids are as functional as possible despite future challenges.