Identification of Critical Transmission Sections Considering N-K Contingencies Under Extreme Events

Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph and identifying its hanging nodes. The network is then partitioned into several undirected subgraphs based on identified cut points. Each subgraph is transformed into a flow network according to actual power flow data. An efficient minimum cut set search algorithm is developed to locate potential transmission sections. To assess the risk under extreme conditions, a mixed-integer optimization model is formulated to select sections that are vulnerable to overload-induced tripping during N-K line outages caused by natural disasters. Simulation results on the IEEE RTS 24-bus and IEEE 39-bus systems validate the effectiveness and applicability of the proposed method.

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Publication Details

Journal
Energies
Published
2025-08-14
DOI
https://doi.org/10.3390/en18164342
Citations
2
Primary Topic
Canadian Policy and Governance
Type
article
Field-Weighted Citation Impact
17.04

Funders

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article

Identification of Critical Transmission Sections Considering N-K Contingencies Under Extreme Events

2 citations
Energies
Canadian Policy and Governance
17.04
article

Identification of Critical Transmission Sections Considering N-K Contingencies Under Extreme Events

article en
2 citations

Abstract

Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph and identifying its hanging nodes. The network is then partitioned into several undirected subgraphs based on identified cut points. Each subgraph is transformed into a flow network according to actual power flow data. An efficient minimum cut set search algorithm is developed to locate potential transmission sections. To assess the risk under extreme conditions, a mixed-integer optimization model is formulated to select sections that are vulnerable to overload-induced tripping during N-K line outages caused by natural disasters. Simulation results on the IEEE RTS 24-bus and IEEE 39-bus systems validate the effectiveness and applicability of the proposed method.

EnergiesVol. 18(16)
State Grid Corporation of China (China) (CN), Huazhong University of Science and Technology (CN)
State Grid Corporation of China
Climate action
Openalex Percentile: Top 1%
Canadian Policy and Governance
17.04
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