Abstract
For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid.
Highlights
In a large-scale power grid, natural disasters, deliberate assaults, element failures, and other faults may cause large-scale blackouts [1]
The results show that the identified 10 key nodes by the proposed modified PageRank algorithm (PMPR) have more influence on the transmission capacity than the other three methods
Compared with the other two methods, the PMPR proposed in this paper takes topological information, node type, and operating characteristics into account comprehensively, and it is more accurate for identifying key nodes in the power grid
Summary
In a large-scale power grid, natural disasters, deliberate assaults, element failures, and other faults may cause large-scale blackouts [1]. Static analysis methods that identify key nodes in the power grid have gradually grown, for example, complex network centrality in [10], topological and controllability features in [11], and electrical betweenness combined with generation rated capacity and load change in [12,13]. A modified PageRank algorithm is presented to assess the node importance, which considers the characteristics of nodal load properties, transmission ultimate capacity, and model structure [23]. Based on the topological information, the characteristics of node type are introduced in the PageRank algorithm for identifying key nodes of the power grid as follows.
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