Fault Location Scheme for Over-Zone Feeding Operation Condition on High-Speed Railway

Lin, Guosong; Quan, Wei; Tong, Xiaoyang

doi:10.1007/s42835-021-00897-0

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Due to the operating voltage limitation, onboard under-voltage protection systems may disconnect the train from the network in the event of a ground fault. Therefore, conventional location methods do not take into account the impact of train current, assuming it is negligible [11][12][13][14][15][16][17][18]. In [11] and [12], a location method based on the ratios of AT neutral current was proposed, which assumes that the train current is much smaller than the short-circuit current and that the traininduced location error is acceptable.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the variations of fault currents at the AT windings with fault distance were calculated, and a fault‐location algorithm utilizing a look‐up table was proposed [16]. Furthermore, a fault‐location method based on fault conductor currents was reported in [17] and [18], which is suitable for the specific structure of the traction network.…”

Section: Introductionmentioning

confidence: 99%

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Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

Wang,

Li,

Han

et al. 2023

IET Generation Trans & Dist

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With the rapid development of high‐speed railway, the auto‐transformer traction network (ATN) is also expanding. However, due to the improved performance of electric multiple units (EMUs) and the non‐linear relationship between impedance and ground fault distance, distance protection has limited coverage and cannot accurately locate the fault distance by measuring impedance. This paper presents a model of the ATN considering the presence of EMUs, which effectively demonstrates the characteristics of current distribution under the ground faults. Furthermore, a ground fault identification method based on multi‐terminal currents is proposed to enhance the accuracy of fault identification and differentiate faults and EMUs. The mapping relationship between current distribution and fault location is derived, and an accurate fault location method is proposed. Compared to existing methods, this method is not affected by EMUs and AT parameters. The fault identification and location scheme for ATN employs multi‐terminal synchronous information. Finally, through simulation and experimental setups, case studies have been conducted to verify the accuracy and robustness of the proposed method in various fault scenarios. Certainly, this research has significant application prospects in fault analysis and detection for HSR.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

Wang,

Li,

Han

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…References [11,12] proposed new schemes for identification of the section with the ground fault based on AT neutral currents, which is more accurate than the traditional identification method. Lin et al [13] combined the AT neutral current ratio method and the reactance method to realize fault location for the over-zone feeding operation condition in high-speed electrified railways. Considering the structure of Korean railway system, Cho et al [14] proposed a novel fault location method based on the relationship between AT neutral currents and branch currents, which improved the accuracy of fault location for Korean traction power supply system.…”

Section: Introductionmentioning

confidence: 99%

Improved fault location method for AT traction power network based on EMU load test

Lin

Quan

et al. 2022

Rail. Eng. Science

Self Cite

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The autotransformer (AT) neutral current ratio method is widely used for fault location in the AT traction power network. With the development of high-speed electrified railways, a large number of data show that the relation between the AT neutral current ratio and the distance from the beginning of the fault AT section to the fault point (Q–L relation) is mostly nonlinear. Therefore, the linear Q–L relation in the traditional fault location method always leads to large errors. To solve this problem, a large number of load-related current data that can be used to describe the Q–L relation are obtained through the load test of the electric multiple unit (EMU). Thus, an improved fault location method based on the back propagation (BP) neural network is proposed in this paper. On this basis, a comparison between the improved method and the traditional method shows that the maximum absolute error and the average absolute error of the improved method are 0.651 km and 0.334 km lower than those of the traditional method, respectively, which demonstrates that the improved method can effectively eliminate the influence of nonlinear factors and greatly improve the accuracy of fault location for the AT traction power network. Finally, combined with a short-circuit test, the accuracy of the improved method is verified.

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Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach

Wang

D’Ariano

et al. 2023

Transportation Research Part B: Methodological

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Fault Location Scheme for Over-Zone Feeding Operation Condition on High-Speed Railway

Cited by 4 publications

References 7 publications

Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

Improved fault location method for AT traction power network based on EMU load test

Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach

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