Power communication network (PCN) is the cornerstone of the stable operation of smart grid. The evaluation of the essential vulnerability of the PCN based on the node importance is advantageous to the protection of critical nodes and the improvement of the reliability and controllability of the system. So, firstly, this paper establishes a multi-index evaluation system for the analysis of important nodes. Based on the site operation level, site load capacity, network topology, service flow, service quality requirements and security operation requirements, the network characteristics of nodes are divided into four layers to describe. Meanwhile, the characteristics of node topology are subdivided into three dimensions to analyze. Then, fully considering the coupling between power network and communication network, a multi-dimensional and multi-layer node importance (MMNI) model based on entropy weight method is proposed. Combined the long-term and short-term vulnerability loss, the node cross-layer vulnerability loss (CVL) function and network cross-layer loss entropy (CVE) model are proposed. Finally, based on the actual power communication network, under different attacks and fault strategies, the changes of maximum network connectivity and network cross layer loss entropy are compared. It shows the effectiveness and accuracy of the MMNI model for the analysis and evaluation of important nodes and it provides a theoretical basis for key weak point identification and the network optimization. 

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Evaluation of Cross-Layer Network Vulnerability of Power Communication Network Based on Multi-Dimensional and Multi-Layer Node Importance Analysis

The large-scale penetration of distributed generation technology makes the operation of distribution network increasingly complex. It is one of the most effective means to quickly and accurately identify the different importance of each node in the power grid. Combining the structural characteristics of the power grid and the power flow information during operation, this paper designs a node importance identification method based on the combination of text-associated deep walk (TADW) algorithm and a page ranking (PageRank) algorithm. This method first uses TADW to extract the structural and electrical characteristics of the distribution network system, and then uses the PageRank algorithm to sort the importance of distribution network nodes to identify key important nodes in the operation of the distribution network. The method is applied to IEEE examples to prove its accuracy and feasibility. Finally, compared with the traditional PageRank algorithm, the significance of the method put forward in this paper is successfully verified by the network transmission efficiency.

Importance Identification of Distribution Network Nodes based on Improved PageRank Algorithm

There are some critical nodes in power system operation, and the failure of these nodes can easily aggravate the propagation of faults and lead to serious power outage accidents. In order to identify critical nodes in power systems and improve system security, this paper proposes a critical node identification algorithm based on PageRank algorithm. A model of the impact of node failure on system line power is proposed based on tide calculation, and this model is combined with the PageRank algorithm to construct point-to-point links to achieve effective identification of critical nodes. Finally, simulation analysis is performed on IEEE-39 system, and the topology and capacity and load are verified and compared with existing methods by simulating deliberate attacks on critical nodes, which proves that the proposed method is useful for identifying nodes in critical positions in the system and can provide guidance for safe and stable operation of the power system.

Zewei Li

Papers

Identification of Key Nodes Based on PageRank Algorithm