An Efficient Method of Fault Analysis using Artificial Neural Network

Open Access

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

    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

Abstract

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: https://journals.stmjournals.com/ctsp/article=2023/view=90543

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