The Damage Prediction Model for Distribution System Under Extreme Weather Events

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dc.description학위논문(석사)--아주대학교 일반대학원 :에너지시스템학과,2019. 8-
dc.description.tableofcontentsI. Introduction 1 II. Data Analysis 4 II.A. Data for Analysis 4 II.B. Damage Data Overview 5 II.C. Analysis of Tropical Cyclone induced Damages 9 II.C.1. Analysis of the Damage Pattern 9 II.C.2. Track Dependence of Regional Damage 13 II.D. Statistical Analysis 18 II.D.1. Significance Tests 18 II.D.2. Variable Analysis 23 III. Multistage Damage Intensity Prediction Method 29 III.A. Damage Intensity Classification and Minority Compensation 30 III.B. Neural Network 31 III.C. Evaluation Metrics 36 III.D. Numerical Simulation 38 IV. Conclusion 40 References 40-
dc.publisherThe Graduate School, Ajou University-
dc.rights아주대학교 논문은 저작권에 의해 보호받습니다.-
dc.titleThe Damage Prediction Model for Distribution System Under Extreme Weather Events-
dc.contributor.affiliation아주대학교 일반대학원-
dc.contributor.department일반대학원 에너지시스템학과- 8-
dc.description.alternativeAbstractTropical cyclone (TC) is considered as the biggest weather event that can cause severe damage in power distribution grid. The predicting of the TC induced damage plays significant role to prepare the mitigation action or to plan a rapid restoration process. In this manner, this present study focused on understanding and predicting the damages associated with TCs pertaining to the number of affected customers considering TC track. Three types of historical data such as TC data, weather, and distribution grid damages are used in this study. Then, describes and analyzes the historical data and presents the characteristic of damages induced by TCs. The impact of TC track shows by directly comparing the damages incurred in TC landfall regions. Besides, the statistical analysis is presented to validate the track dependence of the damages, and the random forest analysis is used to identify the priorities of TC and weather variables. Lastly, a multistage damage intensity prediction method is proposed utilizing track information as well as the priorities of variables. The proposed method approximately predicts the intensity of damages caused by TC over a considerably wider geographical area.-
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Graduate School of Ajou University > Department of Energy Systems > 3. Theses(Master)
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