Single-Ended Data Based Fault Type Identification in Transmission Line Using DNN

Authors

  • Prajakta Dhole Department of Electrical and Electronics Engineering Chhatrapati Shivaji Maharaj University Mumbai, India
  • Sahebrao Patil Department of Electrical Engineering Bhivarabai Sawant Institute of Technology and Research Pune, India
  • Aboo Bakar Khan Department of Electrical and Electronics Engineering Chhatrapati Shivaji Maharaj University Mumbai, India

Keywords:

Faults, Wavelets, MATLAB/Simulink, Decomposition Coefficients

Abstract

The transmission lines form a very crucial part of any power system network. Hence its reliability of power transmission is equally important. But The chances of fault occurrence probability on transmission lines are quite large. Hence to maintain the reliability of transmission there is a need to classify, detect and isolate the faults that will occur on the transmission line. In this paper the Deep Neural Network based technique is implemented for classifying and locating the fault. The system implemented is a two-bus power system network, the line being 100 km long and working voltage is 132 kV. The input dataset RMS values of voltage and currents corresponding to no fault condition and different fault types including different phases. The model gives results with best accuracy in classifying symmetrical as well as unsymmetrical faults providing an overall greater accuracy for the faults studied.

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Published

13.12.2023

How to Cite

Dhole, P. ., Patil , S. ., & Khan , A. B. . (2023). Single-Ended Data Based Fault Type Identification in Transmission Line Using DNN. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 493–498. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4150

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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