A Dual Power Grid Cascading Failure Model for the Vulnerability Analysis

TL;DR

This paper introduces a novel dual power grid cascading failure model that uses an attention mechanism inspired by Transformer models to identify critical transmission lines and improve grid resilience.

Contribution

It proposes a new approach employing attention mechanisms to learn correlations between transmission lines, enhancing vulnerability analysis of power grids.

Findings

The model effectively identifies critical transmission lines.

Extensive experiments validate the approach's accuracy.

The method improves understanding of cascading failure dynamics.

Abstract

Considering the attacks against the power grid, one of the most effective approaches could be the attack to the transmission lines that leads to large cascading failures. Hence, the problem of locating the most critical or vulnerable transmission lines for a Power Grid Cascading Failure (PGCF) has drawn much attention from the research society. There exists many deterministic solutions and stochastic approximation algorithms aiming to analyze the power grid vulnerability. However, it has been challenging to reveal the correlations between the transmission lines to identify the critical ones. In this paper, we propose a novel approach of learning such correlations via attention mechanism inspired by the Transformer based models that were initially designated to learn the correlation of words in sentences. Multiple modifications and adjustments are proposed to support the attention mechanism producing an informative correlation matrix, the Attention Matrix. With the Attention Ranking algorithm, we are able to identify the most critical lines. The proposed Dual PGCF model provide a novel and effective analysis to improve the power grid resilience against cascading failure, which is proved by extensive experiment results.