Using AI to Capture and Analyze Distribution Asset Data

A recently completed case study highlighted a practical, cost-effective approach for utility asset management using artificial intelligence (AI) to improve data collection and database population. This AI-driven method streamlines the identification of third-part attachments, pole features, and design discrepancies by employing field vehicle mounted cameras for data capture. The approach minimizes human intervention while efficiently identifying outliers and conducting comparisons. In the pilot project with a distribution cooperative, advanced AI technologies were used to gather and analyze distribution line data. The project achieved 90% accuracy in identifying and geo-locating Rural Utilities Service pole-top assemblies on distribution poles, demonstrating feasibility and effectiveness of AI for asset management in rural electric cooperatives. This session presents a comprehensive strategy to address asset management challenges by integrating legacy knowledge with cutting edge AI tools. By leveraging these technologies, utilities can enhance data processing and analysis capabilities, enabling better decision making and improved management of assets.