Time Series-Based Fault Detection and Classification in IEEE 9-Bus Transmission Lines Using Deep Learning

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dc.contributor.authorJiriwibhakorn, Somchat
dc.contributor.authorKanwal, Shazia
dc.date.accessioned2026-08-06T10:49:42Z
dc.date.available2026-08-06T10:49:42Z
dc.date.issued2025-01-01
dc.description.abstractTransmission line faults present a significant threat to the stability of power systems, potentially causing widespread outages. Timely detection of these faults is essential to prevent substantial disruptions in the power supply. This paper explores a time series-based deep learning technique for fault detection and classification in the IEEE 9-bus system. Post asymmetrical fault current and voltage time series data have been used to train a convolutional neural network (CNN), representing normal and faulty conditions, with convolutional and ReLU layers. A fully connected layer is used to detect features without missing critical information of the signal, achieving MSE as zero for fault detection and 0.0149 for fault classification. This demonstrates the effectiveness of CNNs for real-time fault detection and classification in complex power grids. The robustness of the CNN model indicates its potential for deployment in practical applications, enhancing the reliability and resilience of the transmission network. Using deep learning techniques opens opportunities for further improvements in fault detection and location strategies within the power grid.
dc.identifier.citationIEEE Access, 13, 118135-118146, 2025
dc.identifier.doi10.1109/ACCESS.2025.3586045
dc.identifier.issn21693536
dc.identifier.other2-s2.0-105010036396
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16591
dc.sourceIEEE Access
dc.subjectasymmetrical faults
dc.subjectConvolutional neural network
dc.subjectIEEE 9-bus system
dc.subjecttransmission line
dc.titleTime Series-Based Fault Detection and Classification in IEEE 9-Bus Transmission Lines Using Deep Learning
dc.typeArticle

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