Correlation knowledge extraction based on data mining for distribution network planning

Zhifang Zhu; Zihan Lin; Liping Chen; Hong Dong; Yanna Gao; Xinyi Liang; Jiahao Deng

doi:10.1016/j.gloei.2023.08.008

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Correlation knowledge extraction based on data mining for distribution network planning

更新时间：2026-10-05

- Correlation knowledge extraction based on data mining for distribution network planning
- Correlation knowledge extraction based on data mining for distribution network planning
- 全球能源互联网(英文) 2023年6卷第4期页码：485-492
- 作者机构：
  
  1. Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.,Guangzhou,PR China,510000
  2. South China University of Technology, School of Electric Power,Guangzhou,PR China,510000
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2023.08.008
  中图分类号：
- 网络首发：2023-08-31，
  
  纸质出版：2023-08-31
- 稿件说明：
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Zhifang Zhu, Zihan Lin, Liping Chen, 等. Correlation knowledge extraction based on data mining for distribution network planning[J]. 全球能源互联网(英文), 2023,6(4):485-492.

Zhifang Zhu, Zihan Lin, Liping Chen, et al. Correlation knowledge extraction based on data mining for distribution network planning[J]. Global Energy Interconnection, 2023, 6(4): 485-492.
Zhifang Zhu, Zihan Lin, Liping Chen, 等. Correlation knowledge extraction based on data mining for distribution network planning[J]. 全球能源互联网(英文), 2023,6(4):485-492. DOI： 10.1016/j.gloei.2023.08.008.

Zhifang Zhu, Zihan Lin, Liping Chen, et al. Correlation knowledge extraction based on data mining for distribution network planning[J]. Global Energy Interconnection, 2023, 6(4): 485-492. DOI： 10.1016/j.gloei.2023.08.008.

摘要

Abstract

Traditional distribution network planning relies on the professional knowledge of planners

especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors. The inherent laws reflected by the historical data of the distribution network are ignored

which affects the objectivity of the planning scheme. In this study

to improve the efficiency and accuracy of distribution network planning

the characteristics of distribution network data were extracted using a data-mining technique

and correlation knowledge of existing problems in the network was obtained. A data-mining model based on correlation rules was established. The inputs of the model were the electrical characteristic indices screened using the gray correlation method. The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules. Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output. In this study

the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined

and the confidence of the correlation rules was obtained. These results can provide an effective basis for the formulation of a distribution network planning scheme.

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