Achieving Robust and Accurate Power Distribution Grid Damage Forecasting via a Two-Stage Forecasting Method

被引：2

作者：

Oh, Seongmun ^{[1
]}

Yang, Yejin ^{[1
]}

Jung, Jaesung ^{[1
]}

Choi, Min-Hee ^{[2
]}

机构：

[1] Ajou Univ, Dept Energy Syst Res, Suwon, South Korea

[2] Korea Elect Power Corp, Res Inst, Smart Power Distribut Lab, Daejeon, South Korea

来源：

2020 4TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2020) | 2020年

关键词：

damage forecasting; distribution grid damage; machine learning; grid resilience; storm event;

D O I：

10.1109/ICGEA49367.2020.239698

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper presents a method to forecast storm induced power distribution grid damage. Three sets of historical data are used: storm data, local weather data, and power distribution grid damage data from January 2008 to March 2018. Before developing the damage forecasting method, the key explanatory variables are identified by using stepwise regression analysis to develop a simpler and robust forecasting model. Thereafter, this paper proposes a two-stage damage forecasting method. Random Forest (RF) and feed-forward neural network (FFNN) model are used for forecasting grid damages. RF is used to classify the no damage and damage cases before the damage forecasting and then FFNN is used to forecast the number of grid damages only for the damage cases. The actual storm event data is used to verify the proposed method by using Mean Absolute Error (MAE).

引用

页码：153 / 157

页数：5