Staff View: A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plan...

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Main Authors:	Khaleefah, Shihab Hamad, A. Mostafa, Salama, Gunasekaran, Saraswathy Shamini, Khattak, Umar Farooq, Yaacob, Siti Salwani, Alanda, Alde
Format:	Article
Language:	English
Published:	JOIV 2024
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.uthm.edu.my/12408/1/J17875_ceeed647e911c42400e1cfda92100665.pdf http://eprints.uthm.edu.my/12408/
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spelling	my.uthm.eprints.124082025-02-24T02:24:36Z http://eprints.uthm.edu.my/12408/ A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System Khaleefah, Shihab Hamad A. Mostafa, Salama Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Yaacob, Siti Salwani Alanda, Alde TK Electrical engineering. Electronics Nuclear engineering Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models. JOIV 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12408/1/J17875_ceeed647e911c42400e1cfda92100665.pdf Khaleefah, Shihab Hamad and A. Mostafa, Salama and Gunasekaran, Saraswathy Shamini and Khattak, Umar Farooq and Yaacob, Siti Salwani and Alanda, Alde (2024) A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System. International Journal On Informatics Visualization, 8 (2). pp. 812-818.
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Khaleefah, Shihab Hamad A. Mostafa, Salama Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Yaacob, Siti Salwani Alanda, Alde A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
description	Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
format	Article
author	Khaleefah, Shihab Hamad A. Mostafa, Salama Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Yaacob, Siti Salwani Alanda, Alde
author_facet	Khaleefah, Shihab Hamad A. Mostafa, Salama Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Yaacob, Siti Salwani Alanda, Alde
author_sort	Khaleefah, Shihab Hamad
title	A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
title_short	A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
title_full	A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
title_fullStr	A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
title_full_unstemmed	A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System
title_sort	deep learning-based fault detection and classification in smart electrical power transmission system
publisher	JOIV
publishDate	2024
url	http://eprints.uthm.edu.my/12408/1/J17875_ceeed647e911c42400e1cfda92100665.pdf http://eprints.uthm.edu.my/12408/
_version_	1825163112425193472
score	13.239859

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

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