Effect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids

Abstract

Power outages in the distribution grid profoundly impact everyday human activity and economic welfare, as numerous infrastructures rely on uninterrupted power for sustained operation. Over the past decade, there has been a significant focus on using machine learning (ML) to predict outage state of risk (SoR) in both research and applications. One of the main causes of outages is weather conditions causing equipment failure due to wear and tear, as well as lightning strikes, in this paper. Therefore, we analyzed the consequences of selecting various weather-related ML model features on the outage SoR. We first show the outage SoR prediction results with and without considering lightning features, and then rank the SoR prediction performance based on various other weather features.