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Optimal siting of substations of traction power supply systems considering seismic risk

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  • Sun, Xiaojun
  • Feng, Ding
  • Zhang, Qiang
  • Lin, Sheng

Abstract

The traction power supply system (TPSS) is a critical infrastructure that provides electrified railways with power. However, its safe and reliable operation is threatened by earthquake disasters. Due to the strong randomness, uneven spatial distribution, and destructive risk of earthquake disasters, it is crucial to develop reasonable planning schemes to mitigate seismic risks of the system. This paper proposes a substation optimal siting method considering seismic risks. By establishing the chain circuit model of the TPSS and employing the dynamic power flow calculation method, the power loss of the system is obtained. By integrating the earthquake scenario simulation and substation fragility analysis, the substation seismic risk is calculated through Monte Carlo simulation. On this basis, the optimization model for substation site selection is formulated, aiming to minimize both the system power loss and the substation seismic risk while adhering to constraints of construction cost, operating safety, and geographical locations. The Pareto optimal solutions are then obtained. Simulation analysis based on actual system parameters shows that compared with traditional site selection methods, the proposed method can effectively mitigate system seismic risks while considering system power loss, providing theoretical bases for the disaster prevention and mitigation in the TPSS planning.

Suggested Citation

  • Sun, Xiaojun & Feng, Ding & Zhang, Qiang & Lin, Sheng, 2024. "Optimal siting of substations of traction power supply systems considering seismic risk," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007159
    DOI: 10.1016/j.ress.2023.109801
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    References listed on IDEAS

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    1. Zhao, Yan-Gang & Qin, Miao-Jun & Lu, Zhao-Hui & Zhang, Long-Wen, 2021. "Seismic fragility analysis of nuclear power plants considering structural parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Byun, Ji-Eun & Song, Junho, 2020. "Efficient probabilistic multi-objective optimization of complex systems using matrix-based Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. He, Jingran & Gao, Ruofan & Chen, Jianbing, 2022. "A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Venkateswaran V, Balaji & Saini, Devender Kumar & Sharma, Madhu, 2021. "Techno-economic hardening strategies to enhance distribution system resilience against earthquake," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
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    Cited by:

    1. Fadel Miguel, Leandro F. & Beck, André T., 2024. "Optimal path shape of friction-based Track-Nonlinear Energy Sinks to minimize lifecycle costs of buildings subjected to ground accelerations," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Sun, Xiaojun & Lin, Sheng & Feng, Ding & Zhang, Qiang, 2024. "Post-disaster repair optimization method for traction power supply system of electrified railways based on train operation loss," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Wen, Jiayi & Li, Xiaoxuan & Xue, Jingwei, 2024. "Feasibility evaluation of Copula theory for substation equipment with multiple nonlinear-related seismic response indexes," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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