A Robust Fault Diagnosis Scheme using Deep Learning for High Voltage Transmission Line

Abstract

The transmission lines repeatedly face an aggregation of shunt-faults and its impact in the real time system
increases the vulnerability, damage in load, and line restoration
cost. Fault detection in power transmission lines have become
significantly crucial due to a rapid increase in number and length.
Any kind of interruption or tripping in transmission lines can
result in a massive failure over a large area, which necessitates
the need of effective protection. The diagnosis of faults help
in detecting and classifying transients that eventually make
the protection of transmission lines convenient. In this paper,
the authors propose a deep learning-enabled technique for the
detection and classification of transmission line faults. The faulty
information are extracted using Discrete Wavelet Transform
(DWT) and fed into the multilayer perceptron classification
model. The results indicate that the proposed approach is capable
of accurately classifying and detecting faults in transmission line
with high precision.