Evaluation method of distribution network operation status based on local fuzzy measure in boundary region

Q2 Energy
Bing Yu, Peng Xie, Zhonglin Ding, Letian Li, Changan Chen, Chunfeng Jing
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引用次数: 0

Abstract

With the increasing complexity of the distribution network, the proportion of abnormal data in the monitoring data of the distribution network and its daily work is extremely low. Traditional clustering analysis methods are difficult to effectively solve the imbalance problem. Therefore, this paper introduces the influence parameters that can adaptively adjust the cluster center of local samples in the boundary area, and improves the cluster center update formula, and proposes a method of distribution network operation state evaluation based on the local blur measurement of the boundary region. The research results found that the five evaluation indicators of the proposed algorithm were 112, 0, 2, 26, and 5, respectively, all of which were superior to the comparison algorithms. The research results showed that the cluster center update optimization method based on local fuzzy measure in boundary region could effectively reduce the negative impact of the edge region occupied by most clusters on its clustering effect, so that the cluster center was always in an ideal position. At the same time, the example results showed that the research method had a risk prediction of 0.91 for power outage networks, which was close to the real situation and had high accuracy. It can provide reference for the operation and maintenance work of power grid personnel, eliminate hidden dangers in advance, and ensure the safe operation of the power grid.

基于边界区域局部模糊度量的配电网运行状态评价方法
随着配电网的日益复杂,异常数据在配电网监测数据及其日常工作中所占比例极低。传统的聚类分析方法难以有效解决不平衡问题。因此,本文引入了可自适应调整边界区域局部样本聚类中心的影响参数,并改进了聚类中心更新公式,提出了一种基于边界区域局部模糊度测量的配网运行状态评价方法。研究结果发现,所提算法的五项评价指标分别为112、0、2、26、5,均优于对比算法。研究结果表明,基于边界区域局部模糊度量的簇中心更新优化方法能有效降低大部分簇占据的边缘区域对其聚类效果的负面影响,使簇中心始终处于理想位置。同时,实例结果表明,该研究方法对停电网络的风险预测值为 0.91,接近实际情况,具有较高的准确性。可以为电网人员的运行维护工作提供参考,提前消除隐患,确保电网安全运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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