Multi-Scenario and Stochastic Thermo-Electro-Mechanical Modeling of Failure in Power Transmission Lines

TL;DR

The paper tackles long-term reliability of overhead transmission lines under environmental variability and preexisting damage. It introduces a coupled thermo-electro-mechanical model with a phase-field damage and fatigue formulation, and employs the nonintrusive Probabilistic Collocation Method to perform uncertainty quantification, sensitivity analysis, and probability-of-failure assessment. Across three scenarios—high winds, wildfire radiation, and icing—it demonstrates how initial damage and extreme events influence temperature, damage evolution, and failure probability, highlighting the dominant roles of fracture energy and current-related heating. The work offers a framework for evaluating transmission-line risk under rare but catastrophic conditions and points to future enhancements, such as transient dynamics and 3D line representations, to improve reliability assessments.

Abstract

Transmission lines, crucial to the power grid, are subjected to diverse environmental conditions such as wind, temperature, humidity, and pollution. While these conditions represent a consistent impact on the transmission lines, certain unpredictable conditions such as unexpected high wind, wildfire, and icing pose catastrophic risks to the reliability and integrity of the transmission lines. These factors in the presence of initial damage and electrical loads greatly affect the material properties. In this paper, we develop a comprehensive thermo-electro-mechanical model to investigate the long-term effect of unexpected high wind, wildfire, and ice on transmission lines. This study offers an in-depth perspective on temperature and damage evolution within the power lines by incorporating a phase field model for damage and fatigue, alongside thermal and electrical models. We define a state function to assess the failure, considering damage and temperature. We study three scenarios deterministically to establish a basic understanding and analyze the stochastic behavior using the Probabilistic Collocation Method (PCM). We utilize PCM for forward uncertainty quantification, conducting sensitivity analysis, and evaluating the probability of failure. This approach offers an in-depth examination of the potential risks associated with transmission lines under unfavorable circumstances.

Figures (24)

• Figure 4: A one-dimensional representation of an initially damaged transmission line subjected to mechanical tension, heat exchange with the environment, and an electric current.
• Figure 5: Drag coefficient $C_D$ for the transmission line.
• Figure 6: Schematic diagram representing the relationship between the thermal, electrical, and mechanical models, in addition to an abstract, scenario-dependent environmental module that provides input and initial conditions.
• Figure 7: Cross-sectional area variation as a function of $A_\sigma$
• Figure 8: Evolution of field variables over 5 years of interval.