Effective fault detection, classification, and localization are vital for smart grid self-healing
and fault mitigation. Deep learning has the capability to autonomously extract fault …

Given the massive increase in demand for electrical energy, particularly owing to global
climate change and population expansion, as well as the development of complicated …

DC microgrid over the last decade has gained a global paradigm in the power system field.
Through the effective integration of distributed energy resources, significant researchers …

This research focuses on leveraging wavelet transform for fault classification within electrical
power transmission networks. This study meticulously examines the influence of various …

ABSTRACT PV (photovoltaic) systems have been deployed more in the recent years to
support green energy generation. In the recent era, grid tied PV has been sparked by the …

The widespread integration of renewable energy sources to the main electrical grids has led
to the increased adoption of AC microgrids. However, the protection of AC microgrids is a …

Microgrid control and operation depend on fault detection and classification because it
allows quick fault separation and recovery. Due to their reliance on sizable fault currents …

The conventional process diagnostic scheme comprises data acquisition, feature extraction,
and fault classification. However, traditional feature extraction uses signal processing …

Conventional distribution networks have evolved into active distribution networks due to the
high penetration of distributed energy resources. These new networks have several …

A complex protection challenge has hampered the widespread acceptance of DC microgrids
despite their numerous advantages, including increased efficiency and stability, as well as …