Statistical filtering-based least square method for short-circuit capacity identification of traction power supply system

Yang, S; Hu, L; Zhang, W; He, T; Lu, DDC; Wu, M

Statistical filtering-based least square method for short-circuit capacity identification of traction power supply system

Yang, S Hu, L Zhang, W He, T Lu, DDC Wu, M

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2025, 172, pp. 111266
Issue Date:: 2025-11-01

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Field	Value	Language
dc.contributor.author	Yang, S
dc.contributor.author	Hu, L
dc.contributor.author	Zhang, W
dc.contributor.author	He, T
dc.contributor.author	Lu, DDC
dc.contributor.author	Wu, M
dc.date.accessioned	2026-04-29T02:46:21Z
dc.date.available	2026-04-29T02:46:21Z
dc.date.issued	2025-11-01
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2025, 172, pp. 111266
dc.identifier.issn	0142-0615
dc.identifier.uri	http://hdl.handle.net/10453/194825
dc.description.abstract	The short-circuit capacity (SCC) of electrified railways extends beyond power supply and voltage stability, encompassing traffic organization, relay protection, and fault diagnosis. However, due to the dual fluctuations of source and loads, it is difficult to establish Thevenin equivalent model (TEM) and calculate the SCC at feeders of traction power supply system (TPSS). This paper proposes a statistical filtering-based least square method to identify SCC. Two statistical filtering rules are described to improve the consistence between voltage and power variations. The data that satisfy two rules are obtained by power threshold and box plots. It can reduce the error of TEM caused by source-side fluctuations and low voltage-power variation consistency. Preliminary results are obtained by the least square regression and evaluated by the fitting quality. The result with poor fitting quality is modified by probability distribution of historical results. Considering the loose thresholds, Kalman filter is introduced to reduce the error caused by obvious outliers. Finally, the proposed algorithm is validated by simulations and field test evaluated by Jensen-Shannon divergence. The SCC is roughly same as the actual value with an improvement of 15.76% compared to conventional methods. Besides, the sensitivity analysis shows the great generalization performance of the proposed methods.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems
dc.relation.isbasedon	10.1016/j.ijepes.2025.111266
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4601 Applied computing
dc.title	Statistical filtering-based least square method for short-circuit capacity identification of traction power supply system
dc.type	Journal Article
utslib.citation.volume	172
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/International Development Research Network
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-04-29T02:46:16Z
pubs.publication-status	Published
pubs.volume	172

Abstract:

The short-circuit capacity (SCC) of electrified railways extends beyond power supply and voltage stability, encompassing traffic organization, relay protection, and fault diagnosis. However, due to the dual fluctuations of source and loads, it is difficult to establish Thevenin equivalent model (TEM) and calculate the SCC at feeders of traction power supply system (TPSS). This paper proposes a statistical filtering-based least square method to identify SCC. Two statistical filtering rules are described to improve the consistence between voltage and power variations. The data that satisfy two rules are obtained by power threshold and box plots. It can reduce the error of TEM caused by source-side fluctuations and low voltage-power variation consistency. Preliminary results are obtained by the least square regression and evaluated by the fitting quality. The result with poor fitting quality is modified by probability distribution of historical results. Considering the loose thresholds, Kalman filter is introduced to reduce the error caused by obvious outliers. Finally, the proposed algorithm is validated by simulations and field test evaluated by Jensen-Shannon divergence. The SCC is roughly same as the actual value with an improvement of 15.76% compared to conventional methods. Besides, the sensitivity analysis shows the great generalization performance of the proposed methods.

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http://hdl.handle.net/10453/194825