A Novel Chirp Detector Algorithm for Universal Pantograph Arc Detection

Mohamed S. Elbelkasi; Nagy I. Elkalashy; Ebrahim A. Badran; Mansour H. Abdel-Rahman

doi:10.48084/etasr.14604

Authors

Mohamed S. Elbelkasi Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt | Faculty of Engineering, Mansoura National University, Gamasa, Egypt
Nagy I. Elkalashy Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin Elkom, Egypt
Ebrahim A. Badran Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt | Mansoura Higher Institute of Engineering and Technology, Mansoura College, Mansoura, Egypt
Mansour H. Abdel-Rahman Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

Volume: 15 | Issue: 6 | Pages: 29590-29597 | December 2025 | https://doi.org/10.48084/etasr.14604

Received: 8 September 2025 | Revised: 25 September 2025 | Accepted: 10 October 2025 | Online: 15 October 2025

Corresponding author: Mohamed S. Elbelkasi

Abstract

Catenary-pantograph arc faults pose a serious threat to the reliability and safety of electric railways. Faults cause violent transient disturbances, accelerate equipment aging, and disrupt power continuity. Real-time applicability is limited for traditional approaches based on their reliance on image processing or vision-based deep learning and their computation latency is greater than the arc time constant. The paper introduces a novel image-free arc detection algorithm that directly processes measured pantograph current signals without relying on vision data. The novelty of the chirp-inspired algorithm lies in integrating band-pass filtering, differentiation, Hilbert transform envelope extraction, and multi-stage statistical processing to construct an efficient arc detection index. In contrast to existing approaches, the algorithm exploits the inherent physical fingerprints of arc transients in the electrical current waveform, representing the first systematic investigation into current-based arc detection in pantograph–catenary systems. The approach is evaluated across 20 test cases under varying arc time constants and fixed voltages to demonstrate its universality. The algorithm consistently distinguishes arc events from mechanical or noise-induced oscillations, providing a stable baseline after arc extinction and enabling real-time and reliable monitoring. The proposed image-free system surpasses vision-based schemes for arc detection, providing a scalable and feasible solution to leading-edge railway electrification systems.

Keywords:

image-free arc detection, Hilbert transform, pantograph–catenary system, series arc detection, real-time pantograph arc monitoring

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References

G. Gao, T. Zhang, W. Wei, Y. Hu, G. Wu, and N. Zhou, "A pantograph arcing model for electrified railways with different speeds," Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 232, no. 6, pp. 1731–1740, Dec. 2017. DOI: https://doi.org/10.1177/0954409717747754

Y. Yan, H. Liu, L. Gan, and R. Zhu, "A novel arc detection and identification method in pantograph-catenary system based on deep learning," Scientific Reports, vol. 15, no. 1, Jan. 2025, Art. no. 3511. DOI: https://doi.org/10.1038/s41598-025-88109-x

S. Barmada and M. Tucci, “Use of Advanced Signal Processing Techniques for Arcing Detection on AC Pantograph Catenary Systems,” in Proceedings of International Conference on Pantograph-Catenary Interaction Framework for Intelligent Control, Amiens, France, Dec. 2011, pp. 1–7.

Y. Liu et al., "A Novel Arcing Detection Model of Pantograph–Catenary for High-Speed Train in Complex Scenes," IEEE, vol. 72, pp. 1–13, Apr. 2023. DOI: https://doi.org/10.1109/TIM.2023.3267365

S. Huang, Y. Zhai, M. Zhang, and X. Hou, "Arc detection and recognition in pantograph–catenary system based on convolutional neural network," Information Sciences, vol. 501, pp. 363–376, June 2019. DOI: https://doi.org/10.1016/j.ins.2019.06.006

R. Chen, Y. Lin, and T. Jin, "High-Speed Railway Pantograph-Catenary Anomaly Detection Method Based on Depth Vision Neural Network," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, July 2022. DOI: https://doi.org/10.1109/TIM.2022.3188042

Y. Luo, Q. Yang, and S. Liu, "Novel Vision-Based Abnormal Behavior Localization of Pantograph-Catenary for High-Speed Trains," IEEE Access, vol. 7, pp. 180935–180946, Nov. 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2955707

H. A. Darwish, M. Hesham, A.-M. I. Taalab, and N. M. Mansour, "Close Accord on DWT Performance and Real-Time Implementation for Protection Applications," IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 2174–2183, Aug. 2010. DOI: https://doi.org/10.1109/TPWRD.2009.2036401

W. Liu, X. Zhang, Y. Dong, and X. Huang, "Arc fault detection for AC SSPC based on Hilbert-Huang transform," in Proceedings of 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, Oct. 2017, pp. 4104–4109. DOI: https://doi.org/10.1109/IECON.2017.8216704

Darwish, Farouk, Taalab, and Mansour, "Investigation of Real-Time Implementation of DSP-Based DWT for Power System Protection," in Proceedings of IEEE/PES Transmission and Distribution Conference and Exhibition, Dallas, TX, USA, May 2006, pp. 1258–1263. DOI: https://doi.org/10.1109/TDC.2006.1668691

B. Sundararaman and P. Jain, "Fault Detection and Classification in Electrical Power Transmission System Using Wavelet Transform," Engineering Proceedings, vol. 59, no. 1, Dec. 2023, , Art. no. 71. DOI: https://doi.org/10.3390/engproc2023059071

N. I. Elkalashy, M. Lehtonen, H. A. Darwish, A.-M. I. Taalab, and M. A. Izzularab, "DWT-Based Detection and Transient Power Direction-Based Location of High-Impedance Faults Due to Leaning Trees in Unearthed MV Networks," IEEE Transactions on Power Delivery, vol. 23, no. 1, pp. 94–101, Jan. 2008. DOI: https://doi.org/10.1109/TPWRD.2007.911168

D. Uhrlandt et al., "Electrical models of arcs in different applications," PLASMA PHYSICS AND TECHNOLOGY, vol. 11, no. 1, pp. 28–35, May 2024. DOI: https://doi.org/10.14311/ppt.2024.1.28

Z. Wang, Z. Li, C. Han, and F. Guo, "Mathematical Model of Pantograph Arc Based on Probability Distribution of Arc Parameters," IEEE Transactions on Transportation Electrification, vol. 9, no. 2, pp. 2026–2037, Oct. 2023. DOI: https://doi.org/10.1109/TTE.2022.3217049

Y.-J. Liu, G. W. Chang, and H. M. Huang, "Mayr’s Equation-Based Model for Pantograph Arc of High-Speed Railway Traction System," IEEE Transactions on Power Delivery, vol. 25, no. 3, pp. 2025–2027, June 2010. DOI: https://doi.org/10.1109/TPWRD.2009.2037521

Y. Wang, Z. Liu, X. Mu, K. Huang, H. Wang, and S. Gao, "An Extended Habedank’s Equation-Based EMTP Model of Pantograph Arcing Considering Pantograph-Catenary Interactions and Train Speeds," IEEE Transactions on Power Delivery, vol. 31, no. 3, pp. 1186–1194, Nov. 2016. DOI: https://doi.org/10.1109/TPWRD.2015.2500260

Z. Xu et al., "Characteristics of pantograph-catenary arc under low air pressure and strong airflow," High Voltage, vol. 7, no. 2, pp. 369–381, Dec. 2022. DOI: https://doi.org/10.1049/hve2.12180

N. V. Hai, N. V. Tiem, L. H. Lan, and T. H. Vo, "Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 9879–9887, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5443

A. Iturregi, B. Barbu, E. Torres, F. Berger, and I. Zamora, "Electric Arc in Low-Voltage Circuit Breakers: Experiments and Simulation," IEEE Transactions on Plasma Science, vol. 45, no. 1, pp. 113–120, Dec. 2016. DOI: https://doi.org/10.1109/TPS.2016.2633400

H.-J. Odenthal, A. Kemminger, F. Krause, L. Sankowski, N. Uebber, and N. Vogl, "Review on Modeling and Simulation of the Electric Arc Furnace (EAF)," steel research international, vol. 89, no. 1, Nov. 2017, Art. no. 1700098. DOI: https://doi.org/10.1002/srin.201700098