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A Method to Forecast Storm-Caused Distribution Grid Damages Using Cost-Sensitive Regression Algorithm
  • Jufri, Fauzan Hanif ;
  • Oh, Seongmun ;
  • Jung, Jaesung ;
  • Choi, Min Hee
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Publication Year
2019-05-01
Journal
2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, pp.3986-3990
Keyword
Cost-sensitive regressiondamage forecastingdistribution grid damagesgrid resiliencestorm event
Mesh Keyword
Cost-sensitiveCost-sensitive learningDistribution gridExtreme weather eventsgrid resiliencePearson correlation coefficientsRegression algorithmsStorm events
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsEnergy Engineering and Power TechnologyRenewable Energy, Sustainability and the EnvironmentElectrical and Electronic EngineeringControl and Optimization
Abstract
The forecasting of the grid damages caused by an extreme weather event, such as storm, has become an important to improve grid resilience. It can be used to prepare the mitigation actions or to plan a rapid restoration during the storm event. In this manner, this paper presents a methodology to forecast the damages caused by the storm events on the distribution grid. First, it integrates three different types of historical data such as the storm data, local weather, and distribution system damages. Then, this integrated data is used to develop the forecasting model by employing the Pearson Correlation Coefficient (PCC) and regression analysis. The accuracy of the model is improved by applying a cost-sensitive learning algorithm on the regression analysis. The proposed methodology is verified by using the actual data of the storm event occurs in South Korea.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36456
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074902288&origin=inward
DOI
https://doi.org/10.1109/isgt-asia.2019.8880929
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8864995
Type
Conference
Funding
ACKNOWLEDGMENT This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172410100040).
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Jung, Jaesung 정재성
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