Modeling, Simulation, and Dynamic Analysis of Earth-Fault Detection in High-Voltage Transmission Networks
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Page Numbers:
375-386
Digital Object Identifier:
10.58578/mjaei.v2i3.7236
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Authors:
D. M. Nazif1*, Yakubu Barau Bal2, Fatima Muhammad3, Sadik Umar4, Aliyu Abdulrahman5 
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Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
This paper addresses the need for accurate and timely single-line-to-ground (SLG) fault detection in high-voltage transmission systems, particularly to improve grid reliability in developing regions. The research objective is to propose and validate an integrated framework that combines modeling, simulation, and real-time implementation for SLG fault identification and location. Methodologically, a dual-unit detection scheme was developed: a MATLAB/Simulink dynamic model emulating a 132 kV transmission line under diverse fault scenarios, and a microcontroller-based hardware prototype employing voltage and current sensors interfaced with an Arduino Uno and GSM module to detect disturbances and transmit location data; experimental validation involved controlled fault injection, waveform inspection, and fault distance estimation via zero-sequence current and voltage dip analysis. Key findings show high-precision fault location with estimation errors consistently below 0.75% over a 30–300 km range, alongside fast response, accuracy, and cost-effectiveness. The study concludes that the combined software–hardware architecture reliably detects and locates SLG faults. The contribution and implication are a scalable, low-cost approach to reducing fault-related outages and enhancing fault management in transmission networks.
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References
Aker, E., Temitope, I., Abubakar, M. R., & Adedeji, A. (2020). Fault Detection and Classification Using Discrete Wavelet Transform. Energies, 13(1), 243.
Altaie, A. S., Saeed, A. S., Al-Khazraji, H. J., & Hasan, H. T. (2023). Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis. Energies, 16(23), 7914.
Alzyoud, M. E., & Akinwale, T. O. (2022). Performance Evaluation of Conventional Relays Under Evolving Grid Conditions. Int. J. Electr. Power Syst., 118, 105113.
Alzyoud, R. A., Younes, H. A., & Mohammed, M. S. (2022). Modeling and Simulation of Distance Protection for Transmission Lines With Different Grounding Methods. International Journal of Power Systems, 7, 86–100.
El-Moursi, M. S., Sharaf, A. M., & Al-Sunni, F. (2019). A Flexible Fault Detection and Protection Scheme for Transmission Systems. IEEE Transactions on Power Delivery, 34(5), 1931–1939.
Godswill, G. M., Nwachukwu, C. A., & Opara, B. I. (2023). AI-Driven Fault Classification in Nigerian 132 kV Transmission Grid Using Hybrid Neural Networks. Nigerian Journal of Electrical Engineering, 13(1), 15–24.
Porawagamage, G., Perera, S., & Fernando, T. (2024). A Review of Machine Learning Applications in Power System Protection. Frontiers in Smart Grids, 3.
Rahman, M. M., & Iqbal, A. (2018). Design and Implementation of GSM-Based Fault Indication System for Underground Cables. International Journal of Scientific & Engineering Research, 9(5), 13–18.
Sharma, A., & Mahajan, R. (2017). Arduino-Based Underground Cable Fault Detection System. International Journal of Engineering Research & Technology (IJERT), 6(6), 529–533.