A Deep Learning-Based Method for Power System Resilience Evaluation

TL;DR

This paper introduces a deep learning framework that predicts power system resilience using historical outage and weather data, enabling more accurate and region-specific resilience assessments to inform infrastructure investments.

Contribution

It presents a novel deep learning-based approach that integrates diverse data sources for power system resilience evaluation, surpassing traditional statistical and simulation methods.

Findings

Strong agreement between predicted and simulated resilience values.

Effective application to real outage data for actual system assessment.

Framework supports targeted resilience improvement strategies.

Abstract

Power system resilience is vital to modern society, as outages caused by extreme weather can severely disrupt communities. Existing statistical and simulation-based methods for resilience quantification are either retrospective or rely on simplified physical models, limiting their applicability. This paper proposes a deep learning-based framework that integrates historical outage and weather data to predict event-level resilience, measured using the resilience trapezoid method. The trained model is then applied to a benchmark weather dataset to estimate regional resilience, with optional socioeconomic and demographic factors incorporated as weighting terms when policymakers wish to emphasize the needs of specific population groups. The effectiveness of the framework is first validated on simulated outage records, showing strong agreement between predicted and simulated resilience values. It is then applied to real historical outage data to assess the resilience of actual power systems. Beyond evaluation, the results can guide targeted investments in distributed energy resources to improve resilience in vulnerable regions.