Quantum-Inspired Genetic Programming for Single-End Line-to-Ground Fault Location in Transmission Lines with Resilience to High Impedance Faults
Corresponding author: Mohamed S. Zaky
Abstract
Accurately locating faults in transmission lines remains a major challenge, especially when relying on single-end measurements and the presence of High-Impedance Faults (HIFs), which are often difficult to detect using traditional techniques. This paper presents a fault location determination method for transmission lines based on Quantum-Inspired Genetic Programming (QIGP) that does not require a communication link, using measurements from only one end of the line. Current prediction techniques, such as neural networks and multi-layer regression, often rely on parameter tuning or intricate transformations of predictor or outcome variables, but still fail to deliver highly accurate results. The proposed QIGP model enhances the accuracy of fault location estimation, as seen in internal validation analysis. Genetic programming is used to derive a linear equation that improves the precision of fault location estimation. Quantum computing is leveraged to optimize the choice of top-performing solutions while managing parsimony pressure to minimize solution complexity. The results include various fault distances associated with different inception angles, load angles, and fault resistances.
Keywords:
quantum-inspired genetic programming, DFT, transmission lines, fault location, one-terminal measurement, matlabDownloads
References
M. Satea, M. Elsadd, M. Zaky, and M. Elgamasy, ''Reliable High Impedance Fault Detection with Experimental Investigation in Distribution Systems,'' Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17248–17255, Oct. 2024. DOI: https://doi.org/10.48084/etasr.8292
Y. Wang and Q. He, ''Advanced Identification of Traveling Wave Heads for Transmission Line Fault Localization,'' in 2025 5th International Conference on Mechanical, Electronics and Electrical and Automation Control (METMS), Feb. 2025, pp. 593–597.
V. Yuvaraju, S. Thangavel, and M. Golla, ''Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines,'' IEEE Access, vol. 12, pp. 136565–136587, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3464088
R. Muzzammel, R. Arshad, A. Raza, N. Sobahi, and U. Alqasemi, ''Two Terminal Instantaneous Power-Based Fault Classification and Location Techniques for Transmission Lines,'' Sustainability, vol. 15, no. 1, Jan. 2023, Art. no. 809. DOI: https://doi.org/10.3390/su15010809
O. A. Hassen, H. L. Majeed, M. A. Hussein, S. M. Darwish, and O. Al-Boridi, ''Quantum Machine Learning for Video Compression: An Optimal Video Frames Compression Model using Qutrits Quantum Genetic Algorithm for Video multicast over the Internet.,'' Journal of Cybersecurity & Information Management, vol. 15, no. 2, Apr. 2025, Art. no. 43. DOI: https://doi.org/10.54216/JCIM.150205
J. Kh-Madhloom, M. K. A. Ghani, and M. R. Baharon, "Enhancement to the patient’s health care image encryption system, using several layers of DNA computing and AES (MLAESDNA)," Periodicals of Engineering and Natural Sciences, vol. 9, no. 4, pp. 928–947, Dec. 2021. DOI: https://doi.org/10.21533/pen.v9.i4.998
N. R. Ravesh, N. Ramezani, I. Ahmadi, and H. Nouri, ''A hybrid artificial neural network and wavelet packet transform approach for fault location in hybrid transmission lines,'' Electric Power Systems Research, vol. 204, Mar. 2022, Art. no. 107721. DOI: https://doi.org/10.1016/j.epsr.2021.107721
A. R. Adly, S. H. E. A. Aleem, M. A. Elsadd, and Z. M. Ali, ''Wavelet packet transform applied to a series-compensated line: A novel scheme for fault identification,'' Measurement, vol. 151, Feb. 2020, Art. no. 107156. DOI: https://doi.org/10.1016/j.measurement.2019.107156
H. A. Jimenez, D. Guillen, R. Tapia-Olvera, G. Escobar, and F. Beltran-Carbajal, ''An improved algorithm for fault detection and location in multi-terminal transmission lines based on wavelet correlation modes,'' Electric Power Systems Research, vol. 192, Mar. 2021, Art. no. 106953. DOI: https://doi.org/10.1016/j.epsr.2020.106953
M. Farshad and J. Sadeh, ''Accurate Single-Phase Fault-Location Method for Transmission Lines Based on K-Nearest Neighbor Algorithm Using One-End Voltage,'' IEEE Transactions on Power Delivery, vol. 27, no. 4, pp. 2360–2367, July 2012. DOI: https://doi.org/10.1109/TPWRD.2012.2211898
Z. Jiao and R. Wu, ''A New Method to Improve Fault Location Accuracy in Transmission Line Based on Fuzzy Multi-Sensor Data Fusion,'' IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 4211–4220, July 2019. DOI: https://doi.org/10.1109/TSG.2018.2853678
G. Zhang, Q. Zhang, J. Ma, G. Liu, D. Sun, and Z. Zhang, ''Fault location of high voltage overhead transmission line based on ACO-ENN algorithm,'' International Journal of Critical Infrastructures, vol. 19, no. 6, pp. 544–557, Jan. 2023. DOI: https://doi.org/10.1504/IJCIS.2023.134619
S. Verma, P. K. Roy, B. Mandal, and I. Mukherjee, ''Artificial Hummingbird Algorithm-based fault location optimization for transmission line,'' Journal of Engineering and Applied Science, vol. 71, no. 1, July 2024, Art. no. 149. DOI: https://doi.org/10.1186/s44147-024-00475-x
M. Šipoš, Z. Klaić, E. K. Nyarko, and K. Fekete, ''Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm,'' Energies, vol. 14, no. 1, Jan. 2021, Art. no. 255. DOI: https://doi.org/10.3390/en14010255
P. Fleck, B. Werth, and M. Affenzeller, ''Population Dynamics in Genetic Programming for Dynamic Symbolic Regression,'' Applied Sciences, vol. 14, no. 2, Jan. 2024, Art. no. 596. DOI: https://doi.org/10.3390/app14020596
M. A. Elsadd, H. A. Omar, A. R. Adly, and A. F. Zobaa, ''Fault-Locator Scheme for Combined Taba-Aqaba Transmission System Based on New Faulted Segment Identification Method,'' IEEE Access, vol. 11, pp. 104270–104284, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3317134
J. Yang, B. Li, and Z. Zhuang, ''Research of Quantum Genetic Algorith and its application in blind source separation,'' Journal of Electronics (China), vol. 20, no. 1, pp. 62–68, Jan. 2003. DOI: https://doi.org/10.1007/s11767-003-0089-4
A. M. Mohammed, N. A. Elhefnawy, M. M. El-Sherbiny, and M. M. Hadhoud, ''Quantum crossover based quantum genetic algorithm for solving non-linear programming,'' in 2012 8th International Conference on Informatics and Systems (INFOS), Giza, Egypt, Feb. 2012.
M. Brezocnik, M. Kovacic, and L. Gusel, ''Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution,'' Materials and Manufacturing Processes, vol. 20, no. 3, pp. 497–508, May 2005. DOI: https://doi.org/10.1081/AMP-200053541
A. Swetapadma and A. Yadav, ''A Novel Decision Tree Regression-Based Fault Distance Estimation Scheme for Transmission Lines,'' IEEE Transactions on Power Delivery, vol. 32, no. 1, pp. 234–245, Oct. 2017. DOI: https://doi.org/10.1109/TPWRD.2016.2598553
K. Dase, S. Harmukh, and A. Chatterjee, ''Detecting and Locating Broken Conductor Faults on High-Voltage Lines to Prevent Autoreclosing Onto Permanent Faults,'' in 46th Annual Western Protective Relay Conference, Spokane, WA, USA, 2019.
X. Wang and W. Xu, ''A 3rd Harmonic Power Based Open Conductor Detection Scheme,'' IEEE Transactions on Power Delivery, vol. 36, no. 2, pp. 1041–1050, Apr. 2021. DOI: https://doi.org/10.1109/TPWRD.2020.3001075
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Copyright (c) 2025 Mohammed H. Al-Qaraghui, Zienab R. Khaleel, Mohamed S. Zaky, Mahmoud M. Elgamasy, Adel M. Sharaf, Asmaa A. Rady
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