Prediction Model for User Number in Power Outage Caused by Wind Disaster for Distribution Networks Based on High-efficient Data Dimensionality Reduction

Hui HOU; Shaohua ZHU; Jufang YU; Xianqiang LI; Ruizeng WEI; Yong HUANG

doi:10.7500/AEPS20210317005

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Prediction Model for User Number in Power Outage Caused by Wind Disaster for Distribution Networks Based on High-efficient Data Dimensionality Reduction

Basic Research | 更新时间：2025-06-16

- Prediction Model for User Number in Power Outage Caused by Wind Disaster for Distribution Networks Based on High-efficient Data Dimensionality Reduction
- Automation of Electric Power Systems Vol. 46, Issue 7, Pages: 69-76(2022)
- 作者机构：
  
  1.武汉理工大学自动化学院,湖北省武汉市 430070
  2.中国能源建设集团浙江省电力设计院有限公司,浙江省杭州市 310012
  3.广东省电力装备可靠性重点实验室,广东电网有限责任公司电力科学研究院,广东省广州市 510080
- 作者简介：
- 基金信息：
  
  University-Industry Collaborative Education Program of the Ministry of Education(201902056044);China Southern Power Grid Company Limited (No. GDKJXM20198441(036100KK52190053)
- DOI：10.7500/AEPS20210317005
  CLC：
- Received：17 March 2021，
  
  Revised：2021-10-19，
  
  Published Online：25 January 2022，
  
  Published：10 April 2022
- 稿件说明：
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侯慧,朱韶华,俞菊芳等.基于高效数据降维的配电网风灾停电用户数量预测模型[J].电力系统自动化,2022,46(7):69-76.

HOU Hui,ZHU Shaohua,YU Jufang,et al.Prediction Model for User Number in Power Outage Caused by Wind Disaster for Distribution Networks Based on High-efficient Data Dimensionality Reduction[J].Automation of Electric Power Systems,2022,46(7):69-76.
侯慧,朱韶华,俞菊芳等.基于高效数据降维的配电网风灾停电用户数量预测模型[J].电力系统自动化,2022,46(7):69-76. DOI： 10.7500/AEPS20210317005.

HOU Hui,ZHU Shaohua,YU Jufang,et al.Prediction Model for User Number in Power Outage Caused by Wind Disaster for Distribution Networks Based on High-efficient Data Dimensionality Reduction[J].Automation of Electric Power Systems,2022,46(7):69-76. DOI： 10.7500/AEPS20210317005.

摘要

风灾可能会导致配电网大面积停电，有效的配电网停电用户数量预测可以为应急抢修提供辅助指导。文中提出了一种基于高效数据降维的配电网停电用户数量预测模型。首先，基于数据驱动的方法，考虑所有特征变量即全局变量构建了基于随机森林算法的配电网停电用户数量预测模型。然后，利用部分依赖图详细分析了台风期间影响配电网停电用户的多种特征变量与响应变量之间的关系，进行了特征降维，降维后二次建模形成了更高效的配电网停电用户数量预测模型。与考虑全局变量的预测模型相比，特征降维后配电网停电用户数量预测模型在一定程度上减轻了数据收集的工作负担、提高了计算效率，为电网防灾减灾提供了有效依据。

Abstract

Wind disaster may lead to large-scale power failure in distribution networks. Effective prediction of user number in power outage for distribution networks can provide auxiliary guidance for emergency repair. This paper proposes a prediction model for user number in power outage for distribution networks based on high-efficient data dimensionality reduction. First

based on the data-driven method and considering all characteristic variables

i.e.

global variables

a prediction model for user number in power outage for distribution networks based on random forest algorithm is constructed. Then

the relationship between various characteristic variables and response variables affecting power outage users of distribution networks during typhoon is analyzed in detail by using partial dependence plots. The characteristic dimension is reduced and the secondary modeling forms a more efficient prediction model for user number in power outage for distribution networks after dimensionality reduction. Compared with the prediction model considering global variables

the prediction model for user number in power outage for distribution networks after characteristic dimensionality reduction reduces the burden of data collection to a certain extent

improves the calculation efficiency

and provides an effective basis for disaster prevention and mitigation of power grid.

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Keywords

references

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