Neural Network based-Driven Fault Classification for Enhancing Stability in Modern Power Systems

Amol Prabhakar Desale , Prof. Samadhan Patil

Artificial Intelligence (AI), Artificial Neural Network (ANN), Fault classification, Mean squared error, power system stability

Ensuring stability and reliability in modern power systems necessitates prompt and accurate fault classification techniques. This paper presents an Artificial Intelligence (AI)-driven approach for fault classification aimed at enhancing system stability. An Artificial Neural Network (ANN) model, trained using the Levenberg-Marquardt optimization algorithm, is developed to classify various fault types occurring in transmission lines. The model utilizes critical system parameters, including RMS values of three-phase voltages and currents, as well as zero-sequence components. MATLAB/Simulink environment is used to simulate a 3-bus power system where the proposed ANN-based classifier is tested under multiple fault conditions. The results indicate a high classification accuracy, achieving a regression value (R) of 0.9818 and Mean Squared Error (MSE) of 0.16178, thereby demonstrating the model’s robustness and effectiveness. This AI-based classification approach significantly contributes to faster fault identification and improved decision-making, thereby ensuring the stability of the power system.

Published Paper   E-Certificate

"Neural Network based-Driven Fault Classification for Enhancing Stability in Modern Power Systems", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c198-c205, March-2025, Available :https://ijsdr.org/papers/IJSDR2503229.pdf

Volume 10 Issue 3, March-2025

Pages : c198-c205

Paper Reg. ID: IJSDR_301171

Published Paper Id: IJSDR2503229

Downloads: 000170

Research Area: Science and Technology

Country: Raigad, Mumbai, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2503229

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2503229

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition