The continuous development of the power industry has driven the increasing scale and complexity of power grids. There are critical nodes within these grids, the failure of which can easily lead to serious power system accidents. In order to effectively identify the critical nodes in a power grid, a critical nodes identification algorithm considering electrical topology and power flow distribution (ETPD-CNIA) is proposed. First, a node interaction model based on the line outage distribution factors is presented. Then, the model is introduced into the PageRank algorithm to calculate the node importance based on the electrical network topology. Finally, considering the node importance based on both the electrical network topology and the actual power flow distribution by an adaptive coefficient, the critical nodes are identified effectively. Simulations on the IEEE 39-bus system and the IEEE 118-bus system show that, compared with existing algorithms, the proposed ETPD-CNIA is more effective and accurate in identifying critical nodes in the power grid. 

Critical Nodes Identification for Power Grid Based on Electrical Topology and Power Flow Distribution

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. 

/pdf/evaluation-of-cross-layer-network-vulnerability-of-power-1swdpjqj.pdf

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

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community's attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques' design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research.

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Vulnerable Nodes Identification Algorithm Based on Virtual Loop Dependence in Power Communication Networks

This research utilizes advanced machine learning techniques to evaluate node vul- nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con- sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs K-nearest neighbor and median imputation methods to address missing data. Cen- trality metrics are integrated into a single comprehensive score for assessing node criticality, categorizing nodes into four centrality levels informative of vulnerability. This categorization informs the use of traditional machine learning (including XG- Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis. The study not only benchmarks the capabilities of these models in network analy- sis but also explores their potential in identifying critical nodes using features be- yond centrality metrics alone, enhancing their applicability in real-world scenarios. The research addresses a significant gap in effectively assessing the vulnerability of electrical networks, marked by isolated use of traditional centrality metrics and a lack of integration between these combined metrics with both tradiational and ad- vanced machine learning models. The study integrates various centrality measures into a comprehensive metric and advocates for the application of advanced ma- chine learning models, particularly GNNs. It underscores the need for larger and more complex datasets to unlock the full potential of GNNs in network vulnerabil- ity assessments. By comparing the performance of GNN models with traditional machine learning approaches across datasets of different sizes and complexities, the study provides insights into optimizing model selection for network analysis, thereby contributing significantly to the field of network vulnerability assessment.

Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques

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.

Identification of Key Nodes Based on PageRank Algorithm

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Identification of Key Nodes Based on PageRank Algorithm