An Efficient Method of Fault Analysis using Artificial Neural Network

Open Access

Year : 2023 | Volume :11 | Issue : 1 | Page : 9-25

    Vikash Kumar

  1. Shashank Mishra

  1. Student, Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  2. Professor, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India


In the power system, there are many techniques to identify and classify the faults. So, it is utmost important to choose the suitable technique. In this paper, a novel technique based on ANN have been proposed. When abnormal conditions occur in the system, the purposed method identifies and classify the fault to protect the system from the faults and stop from the big hazards. Simulation of purposed Simulink model have been tested for the system. The current waveforms are used to identify and classify the fault by using ANN in the MATLAB software. The different types of symmetrical and unsymmetrical faults such as single line to ground fault, line to line fault, double line to ground fault and three phase faults are identified and classified with the proposed model. The identification, validation of faults, check and regression plot and classification are done by the mean square error (performance), gradient and ‘Mu’ in the ANN. With the help of these parameters, identification and classification is performed.

Keywords: Simulink, Signal, Classification, ANN, Type of fault, Feed forward network, Mu, Feed Back network.

[This article belongs to Current Trends in Signal Processing(ctsp)]

How to cite this article: Vikash Kumar, Shashank Mishra , An Efficient Method of Fault Analysis using Artificial Neural Network ctsp 2023; 11:9-25
How to cite this URL: Vikash Kumar, Shashank Mishra , An Efficient Method of Fault Analysis using Artificial Neural Network ctsp 2023 {cited 2023 May 17};11:9-25. Available from:

Full Text PDF Download

Browse Figures


1. Julio Cesar Stacchini de Souza, A.P. Rodrigues, Marcus Theodor Schilling and Milton Brown Do Coutto Filho, “Fault Location in electrical power system using intelligent systems Techniques”, IEEE Trans. On Power Delivery. Vol 16. No.1.pp.59-67. Jan 2001.
2. S. Saha, M. Aldeen, C.P.Tan, “Fault detection in transmission networks of power systems,” Scince Direct Electrical Power and Energy Systems 33, pp 887–900, 2011.
3. Mayuresh Rao & R.P. Hasabe, “Detection and Classification of Faults on Transmission Line Using Wavelet and Neural Network”, International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE), 2278-8948, Volume-2, Issue-5, 2013.
4. C.L. Wadhwa, “Electrical Power System”, New Age international Publishers, sixth edition.
5. Badri Ram and D N Vishwakarma, “Power System Protection and Switchgear”, “Tata McGraw Hill”, Second edition.
6. R. Kamyab Moghadas and S. Gholizadeh, “A New Wavelet Back Propagation Neural Networks for Structural Dynamic Analysis”, lAENG, Engineering Letters, February 2008.
7. Haykin S, “Neural Networks. A comprehensive foundation”, Macmillan Collage Publishing Company, Inc., 1994, New York.
8. Ebha Koley, Anamika Jain, A.S.Thoke and Abhinav Jain and Subhojit Ghosh, “Detection and Classification of Faults on Six Phase Transmission Line Using ANN”, International Conference on Computer & Communication Technology (ICCCT)-2011, 978-1-4577-1386-611$26.00©2011 IEEE.
9. S.N.Sivanandam and S.N.Deepa,“Principle of Soft Computing”, Wiley -Indian Edition, first edition.
10. M. Ananda Rao and J. Srinivas, “Neural Networks Algorithms an Applications”, Narosa publication, 2005.
11. Himani Mahajan and Ashish Sharma, “Distance protection scheme for transmission line using back propogation neural network”, IJRET: International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308.
12. Singh Rajveer, “Artificial Neural Network &Wavelet Transform for Identification and Classification of Faults in Electrical Power System”, Int. Journal of Engineering Research and Applications ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.1993-1999.
13. K. M. Silva, B. A. Souza, and N. S. D. Brito, “Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN”, IEEE Trans. On Power Delivery, vol. 21, no. 4, pp.2058-2063 October 2006.
14. Math works Inc USA, “Neural Networks Toolbox”.
15. Zadeh Hassan Khorashadi, “An ANN- Based High Impedance Fault Detection Scheme: Design and Implementation”, International Journal of Emerging Electric Power Systems, Volume 4, Issue 2, 2005, Article 1.
16. Eisa Bashier M. Tayeb Orner and A Aziz ARhirn, “Transmission Line Faults Detection, Classification and Location using Artificial Neural Network”, 978-1-4673-6008-11111$31.00 ©2012 IEEE.

Regular Issue Open Access Article
Volume 11
Issue 1
Received May 10, 2021
Accepted May 17, 2021
Published May 17, 2023