A Dual Power Grid Cascading Failure Model for the Vulnerability Analysis
This work addresses power grid vulnerability for utility operators and researchers, offering an incremental improvement by applying transformer-inspired methods to a known bottleneck in cascading failure analysis.
The paper tackles the problem of identifying critical transmission lines in power grids to prevent cascading failures by proposing a novel approach that learns correlations between lines using an attention mechanism, achieving improved resilience as demonstrated through extensive experiments.
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.