Electrical grid inspections and failure prediction

The use of computer vision for asset inspection and maintenance is growing in the electric power industry, driven by the convergence of climate change, aging infrastructure, and labor shortages. With extensive reliance on drones to reduce the need for human inspectors and accelerate data acquistion this approach automates the analysis of visual data, minimizing errors and cost. For instance, nuclear power plants use drones to inspect hard-to-reach areas, while solar companies utilize computer vision for regular panel inspections, significantly improving generating efficiency through improved maintenance. These approaches not only detect current issues but also predict future maintenance needs by identifying patterns from historical data.

As these approaches become more widespread, they will reduce the frequency, duration, and geographic extent of power failures, ensuring essential power for cooling and other climate adaptation measures. Moreover, these methods can support the evolution of a more decentralized, resilient power grid by reducing the effort needed to manage larger, more complex networks.