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Correlation knowledge extraction based on data mining for distribution network planning

2023-11-22

Correlation knowledge extraction based on data mining for distribution network planning

基于数据挖掘的配电网规划问题关联性知识提取研究

Zhifang Zhu¹, Zihan Lin¹, Liping Chen¹, Hong Dong¹, Yanna Gao¹, Xinyi Liang², Jiahao Deng²

1.Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou 510000,P. R. China

2.South China University of Technology, School of Electric Power, Guangzhou 510000, P. R. China

Correlation knowledge extraction based on data mining for distribution network planning

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.

Keywords

Distribution network planning; Data mining; Apriori algorithm; Gray correlation analysis; Chi-square test

Fig. 1 Rule mining flow chart based on Apriori algorithm

本文引文信息

Zhu ZF, Lin ZH, Chen LP, et al. (2023) Correlation knowledge extraction based on data mining for distribution network planning, Global Energy Interconnection, 6(4): 485-492

朱志芳，林紫菡，陈丽萍等 (2023) 基于数据挖掘的配电网规划问题关联性知识提取研究. 全球能源互联网（英文）, 6(4): 485-492

Biographies

Zhifang Zhu

received his master’s degree at Huazhong University of Science and Technology, Wuhan, in 2005. He is working in Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. His research interests is power system planning.

Zihan Lin

received her master’s degree at Zhejiang University, Hangzhou, in 2020. She is working in Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Her research interests is power system planning.

Liping Chen

received her master’s degree at Huazhong University of Science and Technology, Wuhan, in 2008. She is working in Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Her research interests is power system planning.

Hong Dong

received her master’s degree at Xi’an Jiaotong University, Xi’an, in 2008.She is working in Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd. Her research interests is power system planning.

Yanna Gao

received her master’s degree at Tianjin University, Tianjin, in 2009. She is working in Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd. Her research interests is power system planning.

Xinyi Liang

received her bachelor’s degree at North China Electric Power University,Beijing, in 2022, and she is pursuing her master’s degree at South China University of Technology, Guangzhou. Her research interests is power system planning.

Jiahao Deng

received his master’s degree at South China University of Technology,Guangzhou, in 2023. He is working in Shantou Power Supply Bureau of Guangdong Power Grid Co., Ltd. His research interests is power system planning.

编辑：王彦博

审核：王伟

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