Artificial Intelligence Augmented Transmission Line Design

This paper explores the transformative potential of Artificial Intelligence (AI) in revolutionizing examines the transformative potential of Artificial Intelligence (AI) in revolutionizing the field of transmission line engineering. It examines the transition from

"Computer Aided Design" (CAD) to "AI-Augmented Design" (AAD), where algorithms actively explore solution spaces.

This paper dissects the application of Artificial Intelligence across the four primary capital cost drivers of line design: Macroscopic Routing using Reinforcement Learning and Graph Theory;

Micrositing utilizing Genetic Algorithms and semantic segmentation of LiDAR data;

Generative Structural Design via Topology Optimization; and Conductor Selection through multi-objective Pareto analysis.

Additionally, the paper addresses emerging applications in predictive modeling for grid resilience and the use of Large Language Models (LLMs) for automated regulatory compliance.

Finally, it evaluates implementation barriers such as data quality and model interpretability, while emphasizing AI’s critical role in bridging the workforce skills gap through knowledge continuity and interactive decision support. The conclusion states that AAD is critical for bridging the workforce skills gap and delivering the "Generative Grid" – an infrastructure network optimized not only for cost but also for resilience.

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