Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages

The reliability of power distribution systems is vital for maintaining the functionality of modern society. Power transmission failures can result in extensive outages, economic losses, and critical service disruptions. These failures often stem from environmental factors, equipment degradation, human error, and structural vulnerabilities. Traditional prediction techniques—relying on manual inspections and rule-based systems—struggle to address the increasing complexity and scale of modern power grids. This paper proposes an AI-driven approach to predict transmission failures by integrating structural modeling with insights from historical distribution outages. By leveraging machine learning algorithms and real-time data analysis, the proposed model enhances predictive accuracy, enabling proactive maintenance and minimizing unexpected downtimes. This fusion of artificial intelligence and structural reliability modeling marks a significant advancement in the field of power system resilience.

Keywords
Transmission failure, power distribution systems, structural modeling, outage prediction, artificial intelligence, machine learning, grid reliability, preventive maintenance, fault detection, smart grid analytics.