Improving Power Grid Resilience Through Predictive Outage Estimation

TL;DR

This paper introduces a machine learning model using multi-dimensional Support Vector Machine to predict component states in power grids during extreme events, aiming to enhance resilience by enabling predictive resource scheduling.

Contribution

The paper presents a novel multi-dimensional SVM model that incorporates resilience indices and event predictions for proactive power grid management during extreme events.

Findings

Model achieves high accuracy in classifying outage vs. operational states.

Validation through cross-validation and benchmarking confirms robustness.

Numerical simulations demonstrate improved system resilience.

Abstract

In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the event, as well as predicted path and intensity of the upcoming extreme event. The outcome of the proposed model is the classified component state data to two categories of outage and operational, which can be further used to schedule system resources in a predictive manner with the objective of maximizing its resilience. The proposed model is validated using \"A-fold cross-validation and model benchmarking techniques. The performance of the model is tested through numerical simulations and based on a well-defined and commonly-used performance measure.