Machine Learning Model to Forecast Power System Breakdowns

D Sharanya, G Swetha, Raiz Israni, T Mrudula

2023

Abstract

For the modern power system to be protected from transmission line failures, real-time monitoring and quick control are necessary. For power systems to operate with reliability, it is essential to identify and classify fault conditions. The conventional methods for fault diagnosis rely on several scholars have suggested the manually extracted feature of experienced engineers for defect identification and classification. Any analogue circuit’s reliability depends heavily on the capacity to detect problems. Early detection of circuit failures can considerably aid in system maintenance by preventing potentially damaging from the issue. In particular for fault orientations and severity levels, intelligent fault detection still has a significant challenge in accurately finding the emerging micro-fault in the power system. Intelligent fault detection methods based on machine learning are the topic of a research boom in fault diagnosis

Paper Citation

in Harvard Style

Sharanya D., Swetha G., Israni R. and Mrudula T. (2023). Machine Learning Model to Forecast Power System Breakdowns. In Proceedings of the 1st International Conference on Intelligent and Sustainable Power and Energy Systems - Volume 1: ISPES; ISBN 978-989-758-689-7, SciTePress, pages 161-166. DOI: 10.5220/0012540800003808

in Bibtex Style

@conference{ispes23,
author={D Sharanya and G Swetha and Raiz Israni and T Mrudula},
title={Machine Learning Model to Forecast Power System Breakdowns},
booktitle={Proceedings of the 1st International Conference on Intelligent and Sustainable Power and Energy Systems - Volume 1: ISPES},
year={2023},
pages={161-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012540800003808},
isbn={978-989-758-689-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Intelligent and Sustainable Power and Energy Systems - Volume 1: ISPES
TI - Machine Learning Model to Forecast Power System Breakdowns
SN - 978-989-758-689-7
AU - Sharanya D.
AU - Swetha G.
AU - Israni R.
AU - Mrudula T.
PY - 2023
SP - 161
EP - 166
DO - 10.5220/0012540800003808
PB - SciTePress