Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards

Seongmun Oh,Jaesung Jung,Junhyuk Kong,Minhee Choi

doi:10.3390/app10144693

Abstract

This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. Machine learning models can recognize key knowledge from a dataset without any preliminary knowledge about the dataset. Hence, machine learning methods have been utilized for solving power grid-related problems. Two sets of historical data were used herein: Local weather data and power grid outage data. First, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather and outages to characterize the heavy rain events. The analysis results show that multiple weather effects are significant in causing power outages, even under heavy-rain conditions. Furthermore, this study proposes a cost-sensitive prediction method using a support vector machine (SVM) model. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the state of the grid. The developed model was evaluated using G-mean values. The proposed method was verified via actual data of a heavy rain event that occurred in South Korea.

Highlights

The electrical power grid constitutes a vital infrastructural component and serves as an essential foundation for modern life
This study presents an machine learning (ML)-based method for predicting the power grid state subjected to heavy-rain hazards
A search for the C log-scaled in the range between 1e-3 and 1e3 and σ2 log-scaled in the range between 1e-3 and 1e3 was performed to find the best parameters of support vector machine (SVM)

Summary

Introduction

The electrical power grid constitutes a vital infrastructural component and serves as an essential foundation for modern life. The frequency and intensity of severe weather events have increased over recent years [1,2,3,4,5]. The frequency, intensity, and duration of extreme weather are expected to increase further, resulting in large-scale power outages [6] and motivating this study on grid resilience, defined as the ability to withstand and rapidly recover from a severe weather event. The grid state demonstrates how the grid, or its components, performs with respect to severe weather events and it can determine the operating conditions of the grid. The grid is typically designed to operate under a certain weather intensity. If the weather intensity increases beyond the design criteria, there will be an increase in the probability of a power outage. Modeling the grid state is very complex; the formulation of the relationship and its solution is difficult to determine

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Conclusion