In this paper, the authors present a new transfer-learning-based robust technique for
detection and classification of multi-type faults in low-voltage power distribution grids. Three …

Fault classification is important for the fault cause analysis and faster power supply
restoration. A deep-learning-based fault classification method in small current grounding …

Faults in distribution grids cause power interruption and economic losses. A crucial part of
distribution grids protection systems is effectively diagnosing the fault to accelerate the …

Power outages in electrical grids can have very negative economic and societal impacts
rendering fault diagnosis paramount to their secure and reliable operation. In this paper …

This paper presents a real-time fault detection and classification network for power
electronics dominated grids (PEDG). The challenges in detection and localization of faults in …

This paper proposes a fault detection and classification scheme for multi-terminal high
voltage direct current (MT-HVdc) lines by integrating discrete wavelet transform (DWT) multi …

A modern electric power system integrated with advanced technologies such as sensors
and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power …

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 …

The robust and efficient diagnosis of power quality disturbances (PQDs) in electric power
systems (EPSs) is one of the most important steps to protect a power system with minimal …

With the growing capacity of distributed renewable energy resources (RERs) integrated into
distribution grids, the power flow distribution becomes more complex. Traditional fault …