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

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.

Text J17875_ceeed647e911c42400e1cfda92100665.pdf Restricted to Registered users only Download (3MB) | Request a copy

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Electrical power transmission networks; fault detection; classification; neural network; deep learning; CNN; LSTM.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Computer Science and Information Technology > FSKTM
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	20 Feb 2025 02:52
Last Modified:	24 Feb 2025 02:24
URI:	http://eprints.uthm.edu.my/id/eprint/12408

Actions (login required)

View Item