  The recent increase in renewable energy penetration at the distribution level
introduces a multi-directional power flow that outdated traditional fault
location techniques. To this extent, the development of new methods is needed
to ensure fast and accurate fault localization and, hence, strengthen power
system reliability. This paper proposes a data-driven ground fault location
method for the power distribution system. An 11-bus 20 kV power system is
modeled in Matlab/Simulink to simulate ground faults. The faults are generated
at different locations and under various system operational states. Time-domain
faulted three-phase voltages at the system substation are then analyzed with
discrete wavelet transform. Statistical quantities of the processed data are
eventually used to train an Artificial Neural Network (ANN) to find a mapping
between computed voltage features and faults. Specifically, three ANNs allow
the prediction of faulted phase, faulted branch, and fault distance from the
system substation separately. According to the results, the method shows good
potential, with a total relative error of 0,4% for fault distance prediction.
The method is applied to datasets with unknown system states to test
robustness.
