Smart grid fault-identification is a critical aspect
of the protection relay system with the integration of renewable
energy based on photovoltaic-distributed generators. With increasing distributed generators usage in smart grids, conventional relaying techniques suffer from maloperation owing to
the risk of changing fault current levels. Therefore, in this paper,
a discrete wavelet transform (DWT) and a statistical cross-alignment coefficient-based method is proposed to detect and classify different types of faults, considering the dynamic response of
photovoltaic. The proposed protection scheme does not require
any extra-measuring systems, as it relied on the one-ended measurements that are installed at PV-feeder over a moving window,
which are available due to the use of advanced measuring facilities in smart grids. This opens the doors to transferring real-time
data from / to protective relays, and then these datasets are processed for discriminating among various internal fault classes and
external and healthy conditions. Intensive simulation studies are
executed using the PSCAD/EMTDC platform, along with the validation of the proposed scheme. The 300 kW PV panel is connected
to grid though a boost converter and Voltage Source inverter. Results reveal that the application of alienation concept and the differential faulty energy method for approximation coefficient-based
DWT for voltage and current signals shows better performance
in terms of accuracy and computational burden.