Component Outage Estimation based on Support Vector Machine

TL;DR

This paper introduces an SVM-based method to predict power system component outages during hurricanes, aiding in preevent planning and recovery by classifying components into damaged, operational, or uncertain states.

Contribution

It proposes a novel SVM classifier combined with an Event-driven Security-Constrained Unit Commitment model for improved outage prediction and system resilience during extreme weather events.

Findings

SVM classifier effectively categorizes component states.

The integrated model enhances power system resilience.

Experimental results on IEEE 118-bus system validate the approach.

Abstract

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events.