California’s wildfires are 500% larger due to climate change, burning 4.3MM acres in 2020. Increasing the risk of wildfires reaching the power grid and the risks of ignition. Electric utility SCE serves 50,000 sq miles (area equaling Greece), with 14,000 miles of power lines and 300,000 poles in high fire risk areas. Utility line workers relied on ground inspection to monitor risks posed to the grid. Due to the rapid acceleration of climate change, it was becoming increasingly challenging to monitor changing conditions of the grid by foot from below.

Our line workers were previously capturing field inspection images (pole deterioration, cross arm damage, and vegetation overgrowth) and manually uploading to an app for risk review. Far too often, these images were unusable, not machine readable, and less actionable for maintenance and remediation. SCE was determined to capture high quality data inputs for improvements in decision making. To tackle wildfire risk, SCE had to see better, faster, and at scale.

It wasn’t enough to react to wildfires, we needed to outsmart them. We needed a safer, more efficient, future-focused way to monitor the gird and prevent wildfires. We needed to protect line workers from trekking through treacherous terrain. We needed to see wildfire risks like pole deterioration and cross-arm damage. We needed to detect hazards like dry vegetation near power lines. And with the climate crisis accelerating risks posed to the grid, we needed to do this faster than ever before.

We launched a human-centered AR imaging app. We’re training ground inspectors to fly drones. We implemented machine learning and AI to see the grid in real time and anticipate wildfire risks.

One of largest aerial remote sensing programs in the United States, The Prevention Grid is reinventing the way we monitor, repair and maintain the power grid.

In order to reduce the impact of climate change on the power grid, we needed a better way to see risks posed to the grid. Our first challenge was gaining better inspection images. So, we built the Inspect Cam app, with human-centered AR guidance to streamline and improve image capture. Once this proved effective, we invented and prototyped a custom Computer Vision AI layer, to automate inspection questions. Then we established an Aerial Inspection Team so we could see the grid from above. We developed machine learning and proprietary AI to scan thousands of inspection images per day. These new Computer Vision tools increased both accuracy and speed in the inspections process and improved overall image readability and usability across the organization. Coupling AI data analysis and Assisted Reality, we capture high quality images of the grid in real time, in order to spot hazards and prevent wildfires.

The Prevention Grid turned our power grid into a smart wildfire mitigation system. It consists of our Inspect Cam app, drones, machine learning, and proprietary AI to predict and prevent catastrophic wildfire damage. Our Inspect Cam app dramatically improved overall visual data quality, with machine legibility increasing from 32% to 78% using AR. We’re training ground inspectors to fly drones to get a 360 view of thousands of poles, directly from their tablets. We implemented machine learning to scan thousands of live images per day, to detect hazards like dry vegetation near power lines. And we built a proprietary AI to anticipate risks like pole deterioration and cross arm damage. The Prevention Grid enabled better decision-making from improved image analysis.

These operational innovations are changing the way we monitor, maintain, and repair the power grid – turning our power grid into a smart wildfire mitigation system.