Quantify Gas-to-Power Fault Propagation Speed: A Semi-Implicit Simulation Approach

Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the crosssystem propagation speed of these faults using a simulationbased approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faults respectively. The boundary conditions at the fault locations are derived using the method of characteristics. Secondly, we propose utilizing a semi-implicit approach to perform post-fault simulations. The approach, based on the stiffly-accurate Rosenbrock scheme, possesses the implicit numerical stability and explicit computation burdens. Therefore, the high-dimensional and multi-time-scale stiff models can be solved in an efficient and robust way. Thirdly, to accurately locate the simulation events, which cannot be predicted a priori, we propose a critical-time-location strategy based on the continuous Runge-Kutta approach. In case studies, we verified the accuracy and the efficiency superiority of the proposed simulation approach. The impacts of gas faults on gas and power dynamics were investigated by simulation, where the critical events were identified accurately. We found that the fault propagation speed mainly depends on the fault position and is influenced by the pipe frictions. The bi-directional coupling between gas and power may lead to cascading failures.

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Publication Details

Journal
IEEE Transactions on Power Systems
Published
2025-05-22
DOI
https://doi.org/10.1109/tpwrs.2025.3572705
Citations
1
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
1.40
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article

Quantify Gas-to-Power Fault Propagation Speed: A Semi-Implicit Simulation Approach

Chengliang Hao, Suhan Zhang, Baoju Li, Ruizhi Yu et al.
1 citations
IEEE Transactions on Power Systems
Risk and Safety Analysis
1.40
article

Quantify Gas-to-Power Fault Propagation Speed: A Semi-Implicit Simulation Approach

Chengliang Hao, Suhan Zhang, Baoju Li, Ruizhi Yu, Shuai Lu, Yong Sun, Wei Gu
article en
1 citations

Abstract

Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the crosssystem propagation speed of these faults using a simulationbased approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faults respectively. The boundary conditions at the fault locations are derived using the method of characteristics. Secondly, we propose utilizing a semi-implicit approach to perform post-fault simulations. The approach, based on the stiffly-accurate Rosenbrock scheme, possesses the implicit numerical stability and explicit computation burdens. Therefore, the high-dimensional and multi-time-scale stiff models can be solved in an efficient and robust way. Thirdly, to accurately locate the simulation events, which cannot be predicted a priori, we propose a critical-time-location strategy based on the continuous Runge-Kutta approach. In case studies, we verified the accuracy and the efficiency superiority of the proposed simulation approach. The impacts of gas faults on gas and power dynamics were investigated by simulation, where the critical events were identified accurately. We found that the fault propagation speed mainly depends on the fault position and is influenced by the pipe frictions. The bi-directional coupling between gas and power may lead to cascading failures.

IEEE Transactions on Power SystemsVol. 40(6)
Jilin Electric Power Research Institute (China) (CN), Southeast University (CN)
Climate action
Openalex Percentile: Top 16%
Risk and Safety Analysis
1.40
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Quantify Gas-to-Power Fault Propagation Speed: A Semi-Implicit Simulation Approach — Chengliang Hao, Suhan Zhang, et al. · IEEE Transactions on Power Systems (2025) | TGRS Research Map | TGRS