The Damage Prediction Model for Distribution System Under Extreme Weather Events
The Damage Prediction Model for Distribution System Under Extreme Weather Events
- 주제(키워드) Extreme Weather , Statistical Analysis , Grid Resilience , Power Outage
- 발행기관 아주대학교
- 지도교수 정재성
- 발행년도 2019
- 학위수여년월 2019. 8
- 학위명 석사
- 학과 및 전공 일반대학원 에너지시스템학과
- 실제URI http://www.dcollection.net/handler/ajou/000000029176
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Tropical 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.
more목차
I. 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