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Accurately identifying vulnerable transmission lines in power system is paramount for averting cascading failures and guaranteeing the safety and stability of power grid operations. This paper introduces a vulnerability assessment method for transmission lines in power system, which incorporates network topology and system operational states. The enhanced degree index is used to assess the lines’ structural vulnerability. In order to evaluate the variations in power flow during regular system operation or following a failure, the improved power transfer distribution factor (PTDF) and line outage distribution factor (LODF) are used to build the line operational state vulnerability indices. To mitigate the limitations of a single assignment method, subjective indicator weights are determined using the hierarchical analysis method, while objective weights are calculated using the entropy value method. These weights are subsequently distributed using game theory. The effectiveness of the proposed method is verified through simulations on the IEEE 39-bus and IEEE 118-bus systems. The experimental results show that the proposed method has higher precision compared with other methods. And the new index can identify the vulnerable lines of the system, either due to their position in the network or by the power they transmit along the network.

A multi-objective prevention strategy for grid cascading failures based on the screening of vulnerable branches is proposed in this paper for the phenomenon of cascading failures triggered by line faults in power systems. Firstly, an improved line safety index and system safety index are proposed, and the Thiel entropy index is introduced to assess the vulnerability of each line in the power system to quantify the impact on grid operational safety after a line fault being withdrawn from the operation. Then, several branches that have a seriously negative impact on grid operational safety after an initial fault being withdrawn from the operation are screened according to the line vulnerability ranking to establish a set of expected incidents. Finally, a cascading failures prevention and control model for power systems is established which takes into account the system safety index and grid voltage stability, as well as the asility of coping with various initial fault scenarios. The model is solved by using the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ). The simulation analysis based on the IEEE 39-node system shows that the proposed prevention strategy leads to a safe and reasonable generator output solution, and it demonstrates the importance of line load factor differences in the identification of vulnerable lines.

The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems.

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Identification of vulnerable lines in power systems has great meaning to predict cascading failures and prevent the blackouts. A novel approach to identify the vulnerable lines in power systems based on the weighted H-index is proposed. Firstly, to remove the Kirchhoff's law constraint in power system, a second-level correlation network is constructed considering the secondary cascading failure. Then, the relative node strength is defined in the second-level correlation network to improve the identification effectiveness. Secondly, a weighted H-index is proposed, aiming at the disadvantages of the classic H-index only suited for unweighted networks. The improved indicator is applied in the second-level correlation network to identify the vulnerable lines of the power grid. The proposed method is verified in the IEEE-39 bus system and a real-life regional power grid in China. The experimental results show that the proposed method has higher precision compared with other methods. Specifically, the identification accuracy is improved by at least 8.33% than existing methods in the IEEE-39 bus system. In addition, the weighted H-index has higher discrimination and robustness than the classic H-index.

• Branches are screened by a heuristic scheme leveraging triangle structures of the power grid. • The proposed scheme reduces the contingency set with a low risk of missing critical branches. • A parallel graph database computing algorithm is designed for fast triangle detection. • A "Line Outage Severity Factor" is defined to accelerate the performance index calculation. • Techniques for avoiding redundant computations are also introduced. In this paper, a triangle count-based branch contingency screening scheme is proposed for transmission power systems. Different triangle count algorithms are compared. A parallel triangle count algorithm is implemented in the graph database for speed-up. Then, a modified performance index is proposed. A structure-related constant called Line Outage Severity Factor (LOSF) is defined to avoid redundant computation when updating the screening results under different operating points. Numerical acceleration techniques are also leveraged to update the screening results when system topology changes. Experiments of N − 1 and N − 2 analysis on three testbed systems demonstrate that about 20% of contingency cases can be ruled out with relatively low risk in missing critical contingencies, which reduces the computational burdens for succeeding stages of the contingency analysis. [ABSTRACT FROM AUTHOR]