A Comparative Study of Artificial Neural Networks for Fault Detection and Location in Mixed Transmission Lines: a Matlab Vs. Python Vs.R Analysis

EasyChair Preprint 10019

Abstract

Artificial neural networks (ANNs) have been increasingly used for fault detection and location in mixed transmission lines. However, the performance of ANNs for this application may vary depending on several factors, including the choice of programming language. In this study, we conduct a comparative analysis of the accuracy of ANNs developed in Matlab Python AND R for detecting fault type and location in mixed transmission lines. We explore various factors that may affect the performance of ANNs, including the quality of training data, neural network architecture, selection of hyperparameters, and optimization algorithm used for training. We compare the accuracy of ANNs developed in Matlab and Python and analyze the advantages and limitations of each language for this application. Our findings suggest that Matlab, Python AND R can be used effectively for developing ANNs for fault detection and location in mixed transmission lines, with each language offering specific advantages and limitations. This study provides insights into the use of ANNs for fault detection and location in mixed transmission lines and offers guidance on the choice of programming language for this application.

Keyphrases: 132 Kv, Fault, Keras, MATLAB, Pandas, Python, TensorFlow, dplyr, neural network

@booklet{EasyChair:10019,
  author    = {Tarun Sangwan and Naresh Kumar},
  title     = {A Comparative Study of Artificial Neural Networks for Fault Detection and Location in Mixed Transmission Lines: a Matlab Vs. Python Vs.R Analysis},
  howpublished = {EasyChair Preprint 10019},
  year      = {EasyChair, 2023}}