  Recent artificial intelligence-based methods have shown great promise in the
use of neural networks for real-time sensing and detection of transmission line
faults and estimation of their locations. The expansion of power systems
including transmission lines with various lengths have made a fault detection,
classification, and location estimation process more challenging. Transmission
line datasets are stream data which are continuously collected by various
sensors and hence, require generalized and fast fault diagnosis approaches.
Newly collected datasets including voltages and currents might not have enough
and accurate labels (fault and no fault) that are useful to train neural
networks. In this paper, a novel transfer learning framework based on a
pre-trained LeNet-5 convolutional neural network is proposed. This method is
able to diagnose faults for different transmission line lengths and impedances
by transferring the knowledge from a source convolutional neural network to
predict a dissimilar target dataset. By transferring this knowledge, faults
from various transmission lines, without having enough labels, can be diagnosed
faster and more efficiently compared to the existing methods. To prove the
feasibility and effectiveness of this methodology, seven different datasets
that include various lengths of transmission lines are used. The robustness of
the proposed methodology against generator voltage fluctuation, variation in
fault distance, fault inception angle, fault resistance, and phase difference
between the two generators are well shown, thus proving its practical values in
the fault diagnosis of transmission lines.
