Loading...
Fault Detection in Transmission Lines Using CNN
Author(s)
Kanwal, Shazia
Date Issued
January 1, 2024
Type
Conference Paper
Abstract
Transmission line faults pose a significant risk to power systems, potentially leading to widespread outages. Detecting these faults using advanced algorithms is crucial for preventing major disruptions in power supply. In this paper, we work on a fault detection technique for the IEEE 9-bus system based on deep learning. By training a Convolutional Neural Network (CNN) on features extracted from both normal and faulty conditions, we achieve an accuracy of 86%. This high accuracy underscores the potential of CNNs for real-world implementation in fault detection systems. The robustness of the CNN approach suggests its viability for deployment in complex, real-time systems, offering improved reliability and resilience against transmission line faults. Additionally, utilizing deep learning techniques opens avenues for further refinement and optimization of fault detection strategies in the power grid.
Citation
Icitee 2024 Proceedings of the 16th International Conference on Information Technology and Electrical Engineering 2024, 24-29, 2024