  In order to transmit electrical energy in a continuous and quality manner, it
is necessary to control it from the point of production to the point of
consumption. Therefore, protection of transmission and distribution lines is
essential at every stage from production to consumption. The main function of
the protection relays in electrical installations should be deactivated as soon
as possible in the event of short circuits in the system. The most important
part of the system is energy transmission lines and distance protection relays
that protect these lines. An accurate error location technique is required to
make fast and efficient work. Transformer neutral point grounding in
transmission lines affects the operation of the zero component current during
the single phase to ground short circuit failure of a power system. Considering
the relationship between the grounding system and protection systems, an
appropriate grounding choice should be made. Artificial neural network (ANN)
has been used in order to accurately locate short circuit faults in different
grounding systems in transmission lines. Compared with support vector machines
(SVM) for testing inside ANN The transmission line model is made in the
PSCAD-EMTDC simulation program. Data sets were created by recording the image
of the impedance change of the R-X impedance diagram of the distance protection
relay in short circuit faults created in different grounding systems. The
related focal points in the images are given as an introduction to different
ANN models using feature extraction and image processing techniques and the ANN
model with the highest fault location estimation accuracy was chosen.
