With an elevating demand and use of power electronics equipment, green energy and the
development of smart grids, power quality disturbance detection and classification holds …

Nowadays, the main grid is facing several challenges related to the integration of renewable
energy resources, deployment of grid-level energy storage devices, deployment of new …

The recent advances in computing technologies and the increasing availability of large
amounts of data in smart grids and smart cities are generating new research opportunities in …

This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with
convolutional neural network (CNN) for power quality disturbance (PQD) classification …

Voltage sags, as defined by the IEEE Standard, refer to sudden drops in voltage magnitude
that last for a duration greater than 0.5 cycles of the power frequency but less than or equal …

The electric power industry sector has become increasingly aware of how counterproductive
voltage sag affects distribution network systems (DNS). The voltage sag backfires …

This paper presents a real-time robust power system forecasting-aided state estimation
method based on the Bayesian framework, deep learning, and Gaussian mixture model to …

Knowledge of the disturbance of the concerned bus is essential to make decisions to
mitigate voltage sags. The most commonly used method is to estimate the state of voltage …

The microgrid (MG) networks require adaptive and rapid fault classification mechanisms due
to their insufficient kinetic energy reserve and dynamic response of power electronic …

This paper presents a novel hybrid approach combining Gramian Angular Summation Field
(GASF) method with a convolutional neural network (CNN) to classify power quality …