Revolutionizing Fault Detection: A Neural Network Approach for Transmission Lines

Authors

  • Saurabh S. Shingare Sri Satya Sai University of Technology and Medical Sciences, Sehore, Madhya Pradesh, India.
  • Brajesh Mohan Gupta Department of Electrical Engineering, School of Engineering, SSSUTMS, Sehore, Madhya Pradesh, India.
  • Prabodh Khampariya Department of Electrical Engineering, School of Engineering, SSSUTMS, Sehore, Madhya Pradesh, India.
  • Shashikant M. Bakre Department of Electrical Engineering, AISSMS, Institute of Information Technology, Pune, Maharashtra, India.

Keywords:

ANN, EHV, Fault classification, Fault detection, Backpropagation

Abstract

This research concentrates on Artificial Neural Networks (ANN) application in identifying and categorizing faults within Extra High Voltage (EHV) networks. The process entails employing backpropagation algorithms and feedforward networks for individual phases. Diverse ANNs are suggested to handle different types of fault, encompassing LG, LL, LLG, and 3-phase faults. The paper conducts an analysis of networks with diverse configurations, involving varying numbers of layers which are hidden and neurons in it, to guide the selection of NN for each phase. The simulated outcomes illustrate the efficacy of the ANN-based approach in recognizing transmission line issues, producing favorable outcomes.

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References

Ben Hessine, M., & Ben Saber, S. (2014). Accurate fault classifier and locator for EHV transmission lines based on artificial neural networks. Mathematical Problems in Engineering, 2014.

Karić, A., Konjić, T., & Jahić, A. (2018). Power system fault detection, classification and location using artificial neural networks. In Advanced Technologies, Systems, and Applications II: Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) (pp. 89-101). Springer International Publishing.

Leh, N. A. M., Zain, F. M., Muhammad, Z., Abd Hamid, S., & Rosli, A. D. (2020, August). Fault detection method using ANN for power transmission line. In 2020 10th IEEE international conference on control system, computing and engineering (ICCSCE) (pp. 79-84). IEEE.

Eriksson L, Saha MM, Rockefeller GD, “An accurate fault locator with compensation for apparent reactance in the fault resistance resulting from remote-end feed”, IEEE Trans on PAS 104(2), 1985, pp. 424-436.

Saha MM, Izykowski J, Rosolowski E, Fault Location on Power Networks, Springer publications, 2010.

Tayeb, E. B. M. (2013). Faults detection in power systems using artificial neural network. American Journal of Engineering Research, 2(6), 69-75.

Tang Y, Wang HF, Aggarwal RK et al.(2000), “Fault indicators in transmission and distribution systems”, Proceedings of International conference on Electric Utility Deregulation and Restructuring and Power Technologies – DRPT, 2000, pp. 238-243.

Shingare, Saurabh S., Prabodh Khampariya, and Shashikant M. Bakre (2022). "A Survey on the Application of Artificial Neural Network-based Approach for Fault Location in Extra High Voltage (EHV) Network." NeuroQuantology 20, no. 5, 2022, 61-73.

Saurabh S. Shingare, Prabodh Khampariya, Shashikant Bakre (2023), "Application of ANN-Based Approach for Fault Location in Extra High Voltage Networks," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 440-449, 2023.

Shingare, Saurabh S. Khampariya, P., & Bakre, S. M. (2023) “Efficient Fault Detection and Location in Extra High Voltage Networks: An Artificial Neural Network (ANN)-Based Approach.” International Journal of Intelligent Systems and Applications in Engineering, 11(3), 2023, 1051–1060.

Nag, A., & Yadav, A. (2016, July). Fault classification using Artificial Neural Network in combined underground cable and overhead line. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-4). IEEE.

Gayathri, K., & Kumarappan, N. (2015). Double circuit EHV transmission lines fault location with RBF based support vector machine and reconstructed input scaled conjugate gradient based neural network. International Journal of Computational Intelligence Systems, 8(1), 95-105.

Pattanayak, R., Behera, S., & Parija, B. (2019, March). Classification of Faults in a Hybrid Power System using Artificial Neural Network. In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) (pp. 1-4). IEEE.

Asbery, C. W., & Liao, Y. (2020, March). Electric transmission system fault identification using modular artificial neural networks for single transmission lines. In 2020 Clemson University Power Systems Conference (PSC) (pp. 1-7). IEEE.

Alashter, M. A., Mrehel, O. G., & Shamekh, A. S. (2020, September). Design and Evaluation a Distance Relay Model Based On Artificial Neural Networks (ANN). In 2020 6th IEEE International Energy Conference (ENERGYCon) (pp. 685-690). IEEE.

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Published

25.12.2023

How to Cite

Shingare, S. S. ., Gupta, B. M. ., Khampariya, P. ., & Bakre, S. M. . (2023). Revolutionizing Fault Detection: A Neural Network Approach for Transmission Lines. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 779–786. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4320

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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