| 引用本文: | 狄立,郑 征,夏 旻,等.基于快速密度聚类的电力通信网节点重要性评估[J].电力系统保护与控制,2016,44(13):90-95.[点击复制] |
| DI Li,ZHENG Zheng,XIA Min,et al.Node importance evaluation of electric power communication network based on fast density clustering[J].Power System Protection and Control,2016,44(13):90-95[点击复制] |
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| 摘要: |
| 电力通信网的节点重要性评估是电力通信研究的一个重要议题。针对目前电力通信网节点重要性评估存在的连接权值单一以及评价指标单一等问题，利用电力通信网的带宽和距离作为权值，计算电力通信网节点的多种评价指标:节点强度、节点紧密度以及节点的介数。基于电力通信网节点的多种评价指标，利用快速密度聚类方法建立电力通信网的节点重要性评估模型，为电网通信的规划做支撑。通过快速密度聚类方法进行无监督的分类，将节点分为若干个重要性等级。该方法可以有效地改善基于距离的无监督分类方法的不足。利用某省的实际电网通信数据进行检验，验证了该方法在电力通信网中的实用性。 |
| 关键词: 电力通信网 节点重要性 快速密度聚类 无监督分类 节点特性 |
| DOI：10.7667/PSPC151377 |
| 投稿时间：2015-08-06修订日期：2015-10-19 |
| 基金项目:国家自然科学基金(61105115) |
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| Node importance evaluation of electric power communication network based on fast density clustering |
| DI Li,ZHENG Zheng,XIA Min,HU Kai |
(Economics and Technology Research Institute of State Grid Henan Electric Power Company,
Zhengzhou 450052, China;Jiangsu Key Laboratory of Big Data Analysis Technology,
Nanjing University of Information Science and Technology, Nanjing 210044, China) |
| Abstract: |
| Node importance evaluation of electric power communication network is an important topic of power communication studies. For the problem of single connection weights and single evaluation index, the bandwidth and the distance of the electric power communication network are used for the network''s weight, computing the evaluating indicators of power communication network node, including joint strength, precision and node betweenness. Evaluation based on a variety of power communication network node and the fast density clustering method are used to establish the importance of the assessment model for power node communication network, supporting for the planning of grid communications. Using unsupervised classification by fast density clustering method, the node is divided into several fragile levels. This method can effectively improve the unsupervised classification method. The actual data grid communications are tested to verify the usefulness of this method in electric power communication network. This work is supported by National Natural Science Foundation of China (No. 61105115). |
| Key words: electric power communication networks node importance fast density clustering unsupervised classification node characteristic |
