基于数据挖掘的配电网规划相关知识提取

IF 1.9 Q4 ENERGY & FUELS
Zhifang Zhu , Zihan Lin , Liping Chen , Hong Dong , Yanna Gao , Xinyi Liang , Jiahao Deng
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引用次数: 0

摘要

传统的配电网规划依赖于规划人员的专业知识,特别是在分析配电网中存在的问题与关键影响因素之间的关系时。忽略了配电网历史数据所反映的内在规律,影响了规划方案的客观性。为了提高配电网规划的效率和准确性,本研究采用数据挖掘技术提取配电网数据的特征,获得配电网中存在问题的相关知识。建立了基于关联规则的数据挖掘模型。模型的输入是采用灰色关联法筛选的电特性指标。利用Apriori算法从配电网运行数据中提取相关知识,得到强相关规则。采用提升度检验和卡方检验验证模型输出强相关规则的合理性。本研究确定了不同地区配电网馈线重负荷或过载问题与相关特征指标之间的相关关系,并获得了相关规则的置信度。研究结果可为配电网规划方案的制定提供有效依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Correlation knowledge extraction based on data mining for distribution network planning

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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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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