Severe weather events pose a significant threat to power grids, causing extensive damage that can result in prolonged outages and dangerous conditions. The speed at which grid operators can assess damage and prioritize repairs is crucial for efficient restoration. Traditional methods for post-storm damage assessment, however, often involve slow and labor-intensive processes that can delay recovery efforts.

This case study highlights how one of our customers in Sweden used Arkion’s AI-powered post-storm inspection solution to improve the speed, efficiency, and safety of their storm recovery efforts, while comparing the results to the regular manual inspection process.

The Manual Process: Drone Fly-Over

In the traditional process, drones were used to fly over the affected grid areas, with pilots manually observing and identifying damage as they flew. Afterward, the issues were compiled into a list, detailing the location and type of damage. This list was then shared with the customer, who would use it to organize on-the-ground inspections and repairs.

AI-Powered Post-Storm Inspections

In contrast, Arkion’s AI-powered solution streamlined the entire process. The customer deployed the AI platform during the same storm recovery effort, and the difference was remarkable:

1. Data Capture – Drones were deployed to collect high-resolution images of the affected areas. Unlike the helicopter fly-over, which could only provide a broad overview, the drones captured detailed, high-quality images of the grid and its components, including hard-to-reach areas. This allowed for a comprehensive and precise assessment of the damage.
2. AI-Powered Analysis – Arkion’s advanced AI models processed the images almost instantaneously, detecting critical defects with exceptional accuracy. The AI identified 74% of all known defects, including severe damage that required immediate attention. In contrast to the manual process, where teams might miss smaller issues, the AI’s ability to detect even subtle problems greatly improved the accuracy of the assessment.
3. Human Verification – Although the AI performed the heavy lifting, expert teams reviewed and refined the AI findings, ensuring that the results were both accurate and actionable. This human verification step ensured precision without slowing down the process.
4. Actionable Insights – The AI generated a prioritized list of critical defects, allowing the customer’s field teams to focus on the most urgent issues first. This meant that repairs could be addressed quickly and in the right order, minimizing downtime and restoring power more efficiently.
5. Continuous Improvement – With each storm recovery effort, the AI models are continually refined and enhanced, learning from the data to improve their performance for future events.

The Results: AI vs. Manual Inspection

The results of using Arkion’s AI-powered solution in comparison to the traditional manual process were clear, especially when applied to a 141 km long stretch of the grid:

• Speed – AI-powered inspections enabled teams to cover vast areas of the grid at speeds of up to 35 km/h. In comparison, the manual process, limited by the helicopter fly-over, required additional time for on-the-ground inspections, making it a far slower process overall.
• Efficiency – Arkion’s AI reduced the image review workload by an impressive 95%. The AI automatically filtered out non-essential data, allowing human reviewers to focus on the most critical issues. In contrast, the manual process required extensive manual review of the affected areas, consuming much more time and resources.
• Accuracy – The AI’s ability to identify 74% of known defects helped operators prioritize repairs quickly and efficiently. The manual process, while effective in identifying some defects, often missed smaller issues or required additional rounds of inspections to catch what was missed the first time.

The Key Benefits: Safety, Speed, and Quality

Beyond the efficiency and accuracy improvements, Arkion’s AI-powered solution also brought significant safety and quality benefits:

• Safety – Rapid damage assessment is critical when dealing with dangerous situations such as broken power lines or downed wires. Arkion’s AI allowed for a quick overview of the damage, reducing the time that field teams spent in potentially hazardous conditions. This not only improved safety for workers but also minimized risks to the public.
• Speed – Traditional manual inspection methods could take days to complete, especially when large areas were affected. With AI-powered analysis, the time needed for inspections was reduced from days to mere hours, allowing the recovery process to begin much faster.
• Quality – By automatically identifying and prioritizing critical defects, the AI minimized the need for manual review. This allowed teams to focus on high-priority issues first, leading to more informed decisions and quicker recovery actions.

Conclusion: The Future of Post-Storm Inspections

This case study clearly demonstrates the transformative impact of AI on post-storm grid inspections. By integrating Arkion’s AI-powered platform into their storm recovery efforts, our customer was able to significantly reduce inspection times, improve the accuracy of damage assessments, and speed up the recovery process. The result was faster, safer, and more efficient power restoration.

Arkion’s AI analytics offer grid operators a scalable, precise, and rapid method for post-storm damage assessment. Whether responding to minor weather disturbances or major storms, our AI-powered solution enables utilities to make data-driven decisions that optimize recovery efforts, reduce downtime, and maintain grid resilience when it matters most.

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