全球能源互联网（英文）2023，Vol.6Issue(4)：485-492,8.DOI:10.1016/j.gloei.2023.08.008
基于数据挖掘的配电网规划问题关联性知识提取研究
Correlation knowledge extraction based on data mining for distribution network planning
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.关键词
配电网规划/数据挖掘/Apriori算法/灰色关联分析/卡方检验Key words
Distribution network planning/Data mining/Apriori algorithm/Gray correlation analysis/Chi-square test引用本文复制引用
朱志芳,林紫菡,陈丽萍,董红,高艳娜,梁歆怡,邓嘉浩..基于数据挖掘的配电网规划问题关联性知识提取研究[J].全球能源互联网（英文）,2023,6(4):485-492,8.基金项目
This work was supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002). （GZHKJXM20210043-080041KK52210002）
