Article: Vulnerable sections identification of distribution networks based on hybrid graph neural networks Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.32 pp.55 - 81 Abstract: Aiming at the voltage limit violations and line overloads caused by high-penetration renewable energy integration, this paper proposes a comprehensive identification method for weak links in distribution networks that integrates dual features of operational status and topological structure. A hybrid Copula function characterises the spatiotemporal correlation of photovoltaic output, and adaptive importance sampling generates typical scenarios. Probabilistic power flow calculates the violation probabilities of node voltage and line current. State and supply vulnerability indices based on utility functions are constructed to quantify voltage instability risk and nodal topological criticality, respectively. A graph neural network is then employed to integrate topological information for comprehensive weak-link identification. Simulation results based on the IEEE 33-node system demonstrate that the proposed method effectively combines operational and topological information, providing a reference for identifying weak links and optimising energy storage configuration in new-type distribution networks. Inderscience Publishers - linking academia, business and industry through research

Title: Vulnerable sections identification of distribution networks based on hybrid graph neural networks

Authors: Junwei Zhu; Feng He; He Zhang; Jianbin Xu; Wei Lu; Ning Chen

Addresses: Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China ' Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China ' Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China ' Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China ' Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China ' Putian Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Putian 351100, China

Abstract: Aiming at the voltage limit violations and line overloads caused by high-penetration renewable energy integration, this paper proposes a comprehensive identification method for weak links in distribution networks that integrates dual features of operational status and topological structure. A hybrid Copula function characterises the spatiotemporal correlation of photovoltaic output, and adaptive importance sampling generates typical scenarios. Probabilistic power flow calculates the violation probabilities of node voltage and line current. State and supply vulnerability indices based on utility functions are constructed to quantify voltage instability risk and nodal topological criticality, respectively. A graph neural network is then employed to integrate topological information for comprehensive weak-link identification. Simulation results based on the IEEE 33-node system demonstrate that the proposed method effectively combines operational and topological information, providing a reference for identifying weak links and optimising energy storage configuration in new-type distribution networks.

Keywords: hybrid Copula; probabilistic power flow; graph neural network; GNN; vulnerability identification; state vulnerability; structural vulnerability.

DOI: 10.1504/IJICT.2026.152863

International Journal of Information and Communication Technology, 2026 Vol.27 No.32, pp.55 - 81

Received: 12 Nov 2025
Accepted: 22 Dec 2025
Published online: 13 Apr 2026 *

Title: Vulnerable sections identification of distribution networks based on hybrid graph neural networks

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