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As climate conditions intensify and electrical infrastructure expands into high-risk areas, the challenge of wildfire prevention has become both urgent and complex. This whitepaper outlines how Eaton is leveraging digital innovation, AI and collaborative research to transform wildfire mitigation strategies for utilities and critical facilities.
Wildfires have more than doubled in reach over the past two decades, causing widespread environmental damage, economic loss and threats to public safety. Millions of acres burn annually, with powerline-related faults emerging as a major ignition source in many regions. Today, the US faces an average of over 61,000 wildfires each year, burning about 7.2 million acres. This rising threat underscores the urgent need for faster and more accurate detection of electrical faults that can spark fires.
Traditional grid protection systems were not designed to detect subtle, evolving fault conditions that can lead to ignition. While utilities have relied on measures such as public safety power shutoffs, these approaches are disruptive and not sustainable in the long run. Digital, data-driven solutions are essential to reduce wildfire risk while maintaining grid reliability.
HiZ faults occur when conductors come in contact with trees, ground surfaces or damaged equipment, producing low-level currents that resemble normal load activity. These faults are notoriously difficult to detect because their electrical signatures vary based on vegetation type, soil conditions, humidity, system voltage and weather. When left undetected, HiZ faults can generate arcing and ignite surrounding fuel.
To address this challenge, Eaton partnered with the U.S. Army Corps of Engineers and the National Renewable Energy Laboratory (NREL) to develop an AI-based, data-driven HiZ detection solution. Through hundreds of controlled laboratory experiments, simulations and field data collection, the team captured real-world fault signatures, creating one of the most comprehensive HiZ data libraries to date.
Eaton’s solution combines integrated high-fidelity sensing, machine learning analytics and edge-based processing to deliver highly accurate fault detection. Advanced AI models analyze high-fidelity current and voltage data directly at grid-edge devices, enabling rapid detection without reliance on continuous communications. In laboratory testing, the technology has demonstrated greater than 90% detection accuracy.
The technology is now being validated through utility pilots across North America, deployed on live distribution systems using Eaton's grid-edge equipment. These pilots provide critical insight into real-world operating conditions, help reduce false positives and refine system performance, bringing the solution closer to commercial deployment.
Wildfire prevention is not a challenge any single organization can solve alone. Eaton’s approach emphasizes close collaboration with utilities, government agencies, research institutions national labs and industry partners to accelerate innovation, validate results and scale solutions that strengthen grid resilience.
Eaton is committed to developing future-proof technologies that address global power management challenges and advancing wildfire mitigation through sustained investment in research, digitalization and AI-driven technologies. By integrating intelligence directly into grid infrastructure, Eaton aims to help utilities reduce wildfire risk, enhance situational awareness and build a safer, more resilient energy future.
Souvik Chandra, PhD, Senior Specialist Engineer, Eaton
Madhab Paudel, PE, Lead Distribution, Protection Engineer, Eaton
Nisar Baloch, PE, Global Product Manager for Overhead Switchgear and Control, Eaton
Xiangying (Linda) Meng, PhD, Data Science Specialist, Eaton
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