A Thermo-Electro-Mechanical Model for Long-Term Reliability of Aging Transmission Lines

TL;DR

This paper introduces a comprehensive thermo-electro-mechanical model for analyzing the long-term reliability of overhead transmission lines, incorporating damage, fatigue, and stochastic uncertainty to predict failure risks under various conditions.

Contribution

It presents a novel coupled phase-field model with probabilistic analysis tools for detailed long-term failure prediction of transmission lines.

Findings

The model accurately predicts temperature evolution and failure probabilities.

PCM efficiently quantifies the impact of uncertain parameters.

Sensitivity analysis identifies key factors influencing reliability.

Abstract

Integrity and reliability of a national power grid system are essential to society's development and security. Among the power grid components, transmission lines are critical due to exposure and vulnerability to severe external conditions, including high winds, ice, and extreme temperatures. The combined effects of external agents with high electrical load and presence of damage precursors greatly affects the conducting material's properties due to a thermal runaway cycle that accelerates the aging process. In this paper, we develop a thermo-electro-mechanical model for long-term failure analysis of overhead transmission lines. A phase-field model of damage and fatigue, coupled with electrical and thermal modules, provides a detailed description of the conductor's temperature evolution. We define a limit state function based on maximum operating temperature to avoid excessive overheating and sagging. We study four representative scenarios deterministically, and propose the Probabilistic Collocation Method (PCM) as a tool to understand the stochastic behavior of the system. We use PCM in forward parametric uncertainty quantification, global sensitivity analysis, and computation of failure probability curves in a straightforward and computationally efficient fashion, and we quantify the most influential parameters that affect the failure predictability from a physics-based perspective.