CNN-Based Transformer Model for Fault Detection in Power System Networks
National Institute of Technology Calicut · Indian Institute of Technology Kanpur
Abstract
Fault detection and localization in electrical power lines has long been a crucial challenge for electrical engineers as it allows the detected fault to be isolated and recovered promptly. These faults, if neglected, can rupture the normal operation of the network and drastically damage the power lines and the equipment attached to it. The wastage of power and money due to these faults can be harmful to the economy of an industry or even a country. Therefore, efficient fault detection mechanisms have become crucial for the well-being of this power-hungry world. This research presents an end-to-end deep learning strategy to detect and localize symmetrical and unsymmetrical faults as well as high-impedance…
Citation impact
- FWCI
- 39.38
- Percentile
- 100%
- References
- 44
Authors
4Topics & keywords
- Transformer
- Deep learning
- Encoder
- Convolutional neural network
- Computer science
- Artificial neural network
- Fault detection and isolation
- Electric power system
- Industry, innovation and infrastructure