Every year, tens of thousands of wildfires sweep across the United States, burning millions of acres of land, destroying homes and businesses, and claiming lives. The human cost is devastating, but the environmental cost is staggering, too—forests that take centuries to grow vanish in days, wildlife lose their habitats, and carbon emissions surge into the atmosphere, accelerating climate change.
While wildfires can be sparked by lightning strikes, droughts, or even a single careless match, a significant percentage of them are caused by something many of us rarely think about: electrical infrastructure. Power lines, transformers, and other equipment are exposed to the elements year after year. As they age and degrade, the risk of failure increases. A single downed wire can send sparks into dry grass, and in an instant, a wildfire is born.
The challenge is deceptively simple: how do you detect the problem before disaster strikes? For decades, the answer has eluded engineers. But now, a new player is stepping in—artificial intelligence.
The Invisible Danger of High-Impedance Faults
To understand the problem, imagine a power line falling to the ground. You might think it would cause an obvious surge or outage, something that would trip alarms and shut systems down. But not all faults announce themselves so loudly.
A high-impedance fault—known as a HiZ fault—occurs when a live wire touches the ground or another surface in such a way that the electrical current is weak, almost like a whisper. The energy released is often too small for traditional systems to detect, yet still strong enough to create sparks. And sparks, in the wrong conditions, are enough to ignite a fire.
This is the kind of hidden threat that makes wildfires linked to power lines so dangerous. They often strike without warning, leaving communities little time to react. Detecting these faint electrical signatures before they escalate has been one of the most difficult challenges in modern grid management.
Enter Artificial Intelligence
Researchers at the National Renewable Energy Laboratory (NREL) believe they’ve found a solution by turning to artificial intelligence. Their project uses machine learning—a branch of AI that allows computers to learn patterns from data—to recognize the subtle signs of HiZ faults that would normally go unnoticed.
Instead of relying on rigid rules, the system trains artificial neural networks (ANNs), which mimic the way the human brain learns and adapts. By feeding the system with massive amounts of data from real-world and simulated scenarios, these ANNs learn to identify the electrical “fingerprints” of a HiZ fault.
When deployed, the system could act as an early-warning system for utility companies. Detect a spark before it ignites. Send a crew to the site. Stop the fire before it spreads.
As Richard Bryce, senior researcher in power systems at NREL and lead on the project, put it: “The intention here is to enhance resilience in the power system and to enable faster responses during extreme events.”
Building a Smarter Grid
The key to this innovation lies in collaboration. To build and train their AI system, NREL partnered with Eaton, a multinational power management company. Together, they recreated a wide range of real-world scenarios, accounting for different soil types, vegetation, moisture levels, and even tree species commonly found across the United States.
Every scenario was a piece of the puzzle. What happens when a power line falls onto gravel compared to grass? How does moisture in the soil affect the fault signature? By running thousands of these tests, Eaton provided NREL with invaluable data.
But to truly push the boundaries, NREL turned to its high-performance computing resources and simulation tools. Using a platform called PSCAD (Power Systems Computer Aided Design), they were able to expand Eaton’s real-world test data into vast, simulated datasets—essentially generating fault scenarios that would be impossible to recreate in the field.
The result was a digital training ground where AI could learn from countless variations of HiZ faults. Out of this process, researchers created an ensemble of neural networks, carefully selecting the most effective models to detect faults with remarkable accuracy.
A Firefighter in the Wires
Imagine the impact of such a system on wildfire prevention. Instead of learning about a downed line only after a fire starts, utilities could be alerted within moments of the fault occurring. Resources could be dispatched immediately. Power could be cut to prevent sparks.
The potential is enormous. Not only does this reduce the risk of wildfires, but it also strengthens the grid’s resilience against outages. Customers experience fewer disruptions, emergency services face fewer crises, and communities become safer.
In a way, this technology acts like a firefighter built into the wires themselves—watching, listening, and responding faster than any human ever could.
The Human and Environmental Stakes
The stakes could not be higher. In 2018, California’s Camp Fire—caused by faulty electrical transmission lines—claimed 85 lives, destroyed nearly 19,000 buildings, and burned more than 150,000 acres. It remains the deadliest and most destructive wildfire in the state’s history.
The cost of prevention pales in comparison to the cost of catastrophe. Billions of dollars are lost each year to wildfire damage, not to mention the immeasurable cost of human lives, trauma, and environmental devastation. If AI can stop even a fraction of these disasters, it represents a revolution in how we protect our communities and our planet.
A Future Beyond the United States
Though the project began in the United States, its implications stretch far beyond American borders. Power lines crisscross every continent, and wildfires are not confined to one region. From Australia’s bushfires to Mediterranean forest fires, the dangers are global.
NREL’s team is already collaborating with international partners to adapt and scale the technology, ensuring it can be applied to different climates, vegetation types, and grid infrastructures. The goal is not just to protect one country but to build a global safety net against one of the most destructive forces on Earth.
The Promise and Responsibility of AI
Of course, no technology comes without responsibility. AI is a powerful tool, and deploying it across critical infrastructure requires caution, transparency, and careful oversight. But when used wisely, AI can be more than a tool—it can be a lifeline.
This project is not about replacing humans but empowering them. Utility companies still make the final decisions, crews still respond to emergencies, and policymakers still shape the framework. What AI provides is time—the crucial seconds or minutes that can mean the difference between a minor incident and a raging wildfire.
Looking Ahead
There is a quiet but profound hope woven into this work. For decades, the power grid has been a silent risk factor in wildfire ignition. But with the marriage of advanced computing and human ingenuity, that risk could soon be dramatically reduced.
As Bryce reflected, “We had testing through our partnership with Eaton that provided real data that is experimentally derived, and then we were able to leverage NREL’s high-performance computing and machine learning to provide a solution to utilities which has a very significant, immediate real-world impact.”
The vision is clear: a world where wildfires are less frequent, where communities are safer, and where the power that lights our homes does not also threaten to burn them down.
Conclusion: Sparks of Hope
At first glance, power lines and wildfires may seem worlds apart—one a symbol of human progress, the other a force of natural destruction. But in the fragile intersection between them lies one of the greatest challenges of our time.
By harnessing artificial intelligence, we are not just preventing sparks from becoming infernos. We are proving that technology, when guided by wisdom and compassion, can serve as a guardian of life, land, and future generations.
Wildfires may never be fully eliminated. Nature has always carried fire in its heart. But if AI can help us tame the sparks born of human systems, then perhaps we can rewrite the story—transforming fire from a symbol of devastation into a testament of resilience.
And in that story, every saved forest, every spared community, and every life kept safe becomes a victory not just of science, but of humanity itself.
