Design and Construction of a Prototype Arduino-Based Fault Detection System for the Agu-Awka 132 KV Transmission Line Using GSM Feedback

References

[1] N. Tarun, A. Kumar, V. Reddy, and P. Nair, “Transmission Line Fault Detection Using Arduino with GSM,” Journal of Engineering Sciences, vol. 13, no. 06, pp. 89-94, Jun. 2022.

[2] K. V. Lalitha, D. DevrajSai, A. Meghana, K. Y. Lakshmi, and B. Amarnadh, “A Novel Transmission Line Fault Detection using GSM Technology,” International Journal for Modern Trends in Science and Technology, vol. 8(S05), pp. 89–94, 2022.

[3] G. Jadhav, S. Shinde, R. Jadhav, K. Giri, and N. Dagade, “Transmission Line Fault Detection Using IoT,” EasyChair Preprint 8059, 2022.

[4] E. E. Aker, M. L. Othman, I. Aris, N. I. A. Wahab, H. Hizam, and O. Emmanuel, “Transmission line fault identification and classification with integrated FACTS device using multiresolution analysis and Naïve Bayes classifier,” International Journal of Power Electronics and Drive Systems, 2023.

[5] S. K. Özdemir, A. Yildiz, H. Bozkurt, and E. Ertugrul, “Fault Classification and Precise Fault Location Detection in 400 kV High-Voltage Power Transmission Lines Using Machine Learning Algorithms,” Processes, vol. 13, no. 2, 2025.

[6] S. V. K. Pratap Singh, T. Prasad, S. Kamila, and P. Agnihotri, “Fault Detection and Classification using Wavelet and ANN in DFIG and TCSC Connected Transmission Line,” arXiv:2308.09046, Aug. 2023.

[7] F. Mohammadi Shakiba, S. M. Azizi, M. Zhou, and A. Abusorrah, “Application of Machine Learning Methods in Fault Detection and Classification of Power Transmission Lines: A Survey,” 2023.

[8] H. Yang, Y. Li, X. Zhao, and L. Zhang, “Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning,” Energies, vol. 15, no. 15, 2022.

[9] K. K. Chowdary, K. Rushitha, K. R. Venkata, A. V. Lakshmi, G. V. N. Prasad, and G. Harsha, “Transmission Line Fault Detection by Using Machine Learning Algorithms,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 23S, 2024.

[10] K. Rai, F. Hojatpanah, F. Badrkhani Ajaei, and K. Grolinger, “Deep Learning for High-Impedance Fault Detection Using Convolutional Autoencoders,” arXiv:2106.13276, 2021.

[11] W. Wei, Y. Jinyang, L. Xiaoyang, H. Yingjie, and Q. Huilong, “Research on Fault Diagnosis of Transmission Lines Based on Machine Learning,” Academic Journal of Engineering and Technology Science, vol. 7, no. 3, 2024.

[12] P. V. Dhole, S. N. Patil, and A. Bakar Khan, “Deep Neural Network Based Transmission Line Fault Location Algorithm,” International Journal of Intelligent Systems and Applications in Engineering, 2025.

[13] P. D. Kadao, S. Bhuyarkar, A. Lingayat, M. Ramteke, S. Raut, S. Dholne, and K. Chauhan, “Automatic Transmission Line Fault Detection System Using GSM Technology,” IJRASET, 2024.

[14] A. Kumar, “Three Phase Transmission Line Fault Detection using Arduino,” IJRASET, May 2024.

[15] P. K. Bera, S. R. Pani, C. Isik, and R. C. Bansal, “A Hybrid Intelligent System for Protection of Transmission Lines Connected to PV Farms Based on Linear Trends,” arXiv:2406.13194, 2024.

[16] Y. Xing, H. Zhang, and J. Li, “A Physics-Informed Data-Driven Fault Location Method for Transmission Lines Using Single-Ended Measurements,” arXiv:2307.09740, 2023.

[17] S. Liu, Y. Ma, Z. Wang, K. Li, J. Huang, and L. Bi, “A Novel Traveling Wave Fault Location Method Based on ICEEMDAN-NTEO for Distribution Network,” International Journal of Engineering and Technology Innovation, 2024.

[18] S. Jadhav, S. Bansode, C. Thore, and M. Makwana, “Fault Detection in Transmission Line – Review,” IJRASET, 2024.

[19] C. Ofori, J. C. Attachie, and F. Obeng-Adjapong, “A GSM-Based Fault Detection on Overhead Distribution Lines,” Journal of New Technology and Energy, 2023.

[20] S. Masanta, “Arduino-Enabled Detection of Faults in Power Transmission Lines,” International Journal of Science and Innovative Engineering, vol. 2, no. 10, Oct. 2025.

[21] P. K. Shukla, S. Kumar, and M. Tiwari, “Deep Learning Techniques for Transmission Line Fault Detection and Classification,” Applied Sciences, 2024.

[22] N. P. Patidar, S. Agrawal, and R. A. Gupta, “Modeling and Analysis of Faults in High Voltage Transmission Lines,” International Journal of Electrical Power and Energy Systems, vol. 134, 2022.

[23] S. O. Olayiwola and M. A. Salawu, “Statistical Analysis of Transmission Line Faults in Nigeria’s 132 kV Network,” Nigerian Journal of Technology (NIJOTECH), vol. 41, no. 2, pp. 167–174, 2023.

[24] D. Das, R. K. Aggarwal, and A. T. Johns, “A New Algorithm for Fault Detection on EHV Transmission Lines,” *IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 903–910, 2021

[25] P. Bhalja and R. P. Maheshwari, “High-Speed Distance Relaying Using Discrete Fourier Transform,” IEEE Transactions on Power Delivery, vol. 22, no. 2, pp. 810–818, 2019.

[26] S. Ghosh and D. Lubkeman, “Impedance-Based Fault Location Challenges in Weak Transmission Systems,” Electric Power Components and Systems, vol. 51, no. 1, pp. 45–58, 2023.

[27] R. Mishra, M. Raj, and S. K. Singh, “Microcontroller-Based Fault Detection in High-Voltage Transmission Lines,” International Journal of Advanced Engineering Research and Science, vol. 9, no. 8, 2022.

[28] P. Verma, A. Gupta, and R. Sharma, “GSM-Enabled Fault Reporting System for Smart Grids,” IJRASET, vol. 10, no. 5, 2023.

[29] Y. Xing, H. Zhang, and J. Li, “A Physics-Informed Data-Driven Fault Location Method for Transmission Lines Using Single-Ended Measurements,” *arXiv:2307.09740*, 2023.

[30] S. Liu et al., “Traveling Wave-Based Fault Location in Compensated Networks Using ICEEMDAN-NTEO,” *International Journal of Engineering and Technology Innovation*, 2024.

[31] P. K. Bera, S. R. Pani, and R. C. Bansal, “A Hybrid Intelligent System for Protection of Transmission Lines Connected to PV Farms,” *Applied Sciences*, vol. 14, no. 6, 2024.

@article{1712302,
      author = {Onuabuchi. S. Alegu, Chekwube H. Ubaka, Cosmas. U. Ogbuka, Progress. A. Nnoli},
      title = {Design and Construction of a Prototype Arduino-Based Fault Detection System for the Agu-Awka 132 KV Transmission Line Using GSM Feedback},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {2141-2149},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712302.pdf},
      abstract = {High-voltage transmission systems should be reliable, which can influence the stability of power systems and limit interruptions to energy supply. The Agu-Awka Transmission Company of Nigeria (TCN) substation has an installed capacity of 2 ? 60 MVA at 132/33 kV that links the Onitsha?Enugu transmission corridor to the Awka distribution network. Frequent faults such as single-line-to-ground, double-line, and three-phase short circuits within this corridor often result in prolonged outages and equipment damage due to delayed fault detection and reporting. This paper presents the design and construction of a prototype Arduino-based fault detection system for the Agu-Awka 132 kV transmission line with GSM feedback. The system is designed using current and voltage sensors interfaced with ATmega328p microcontroller to monitor line parameters continuously. When the system detects an abnormal condition beyond a specified limit, it gives a signal to isolate the affected phase through the relay. SMS is sent to the control room for corrective action. Results demonstrate fast detection, accurate fault phase identification, and reliable GSM feedback transmission. This low-cost, scalable solution provides a foundation for smart fault monitoring in Nigeria?s high-voltage transmission network, enhancing operational reliability and reducing downtime across substations such as Agu-Awka.},
      keywords = {Arduino, GSM, Fault Detection, Transmission Line, Electrical Power System.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712302}
  }