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A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes

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  • Liang, Huangbin

Abstract

Earthquakes pose a huge threat to the power system in seismically active regions. Ultra-High Voltage Direct Current (UHVDC) converter stations become integral to modern power grids, especially for long-distance power transmission, and thus understanding and improving their seismic reliability is essential for ensuring the robustness of the power system. This paper presents a comprehensive reliability-based approach to identify critical components within UHVDC converter stations, focusing on seismic reliability. A seismic reliability index is defined as the expected post-earthquake transmission capacity loss, considering both the earthquake probability and the derated capacity under different operation modes. The converter system's seismic reliability model is established based on divide-and-group principles, dividing it into subsystems and deriving an equivalent logical model based on their interdependency. Failure probabilities of subsystems, consisting of wire-interconnected electrical equipment, are determined through finite element models and seismic vulnerability analysis, accounting for wire interaction forces. Advanced sensitivity analysis techniques such as the Morris method and Sobol's analysis identify critical components influencing seismic reliability. A case study on a real-world ±800 kV UHVDC converter station system demonstrates the effectiveness of the proposed approach in enhancing seismic reliability efficiently.

Suggested Citation

  • Liang, Huangbin, 2025. "A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025001802
    DOI: 10.1016/j.ress.2025.110977
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    References listed on IDEAS

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    1. Xue, Zhihang & Wang, Kai & Cao, Di & Zhou, Siyu & Liu, Yu, 2025. "Resilience-based importance measure for ultra-high voltage converter stations under mainshock-aftershock sequences," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    2. Liang, Huangbin & Moya, Beatriz & Chinesta, Francisco & Chatzi, Eleni, 2026. "Resilience-based post disaster recovery optimization for infrastructure system via deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    3. Wu, Siyuan & Chen, Junhan & Xie, Qiang, 2025. "Dynamic Bayesian network-based seismic resilience evaluation for ±800kV UHV converter stations," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    4. Ahumada, Nicolás & Muñoz Gálvez, Juan Pablo & Poulos, Alan & Rojas, Félix & Carlos de la Llera, Juan, 2026. "Seismic fragility estimation of electrical substations accounting for component damage and short circuit faults," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    5. Liang, Huangbin & Moya, Beatriz & Chinesta, Francisco & Chatzi, Eleni, 2026. "A multi-model probabilistic framework for seismic risk assessment and retrofit planning of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 268(C).

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