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Achieving Robust and Accurate Power Distribution Grid Damage Forecasting via a Two-Stage Forecasting Method
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Publication Year
2020-03-01
Journal
Proceedings of 2020 4th International Conference on Green Energy and Applications, ICGEA 2020
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings of 2020 4th International Conference on Green Energy and Applications, ICGEA 2020, pp.153-157
Keyword
Damage forecastingDistribution grid damageGrid resilienceMachine learningStorm event
Mesh Keyword
Explanatory variablesForecasting methodsForecasting modelingMean absolute errorNumber of GridsPower distribution gridsStepwise regression analysisTwo stage damage
All Science Classification Codes (ASJC)
Energy Engineering and Power TechnologyRenewable Energy, Sustainability and the Environment
Abstract
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).
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36571
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084957361&origin=inward
DOI
https://doi.org/10.1109/icgea49367.2020.239698
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9070015
Type
Conference
Funding
This research was supported by Korea Electric Power Corporation. (Grant number: R17XA05-37)ACKNOWLEDGMENT This research was supported by Korea Electric Power Corporation. (Grant number: R17XA05-37).
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Jung, Jaesung  Image
Jung, Jaesung 정재성
Department of Electrical and Computer Engineering
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