Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

Lithium-ion (Li-ion) batteries have been widely applied in industrial applications. It is desired
to predict the health state of batteries to achieve optimal operation and health management …

Facing the difficulty of expensive and trivial data collection and annotation, how to make a
deep learning-based short-term voltage stability assessment (STVSA) model work well on a …

Deep learning (DL) has emerged as an effective solution for addressing the challenges of
short-term voltage stability assessment (STVSA) in power systems; however, existing DL …

The growing energy demand and the emerging environmental concerns across the globe
caused increasing penetration of renewable energy sources (RESs) in distribution systems …

Voltage collapse is a problem that may happen when power systems are overloaded. An
accurate estimation of critical operating conditions is necessary to prevent voltage collapses …

To fully learn the latent temporal dependencies from post-disturbance system dynamic
trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of …

Voltage instability is a serious phenomenon that can occur in a power system because of
critical or stressed conditions. To prevent voltage collapse caused by such instability …

Situation awareness (SA) has been recognized as a critical guarantee for the stable and
secure operation of electric power systems, especially under complex uncertainties after …

Load shedding (LS) is an effective control strategy against voltage instability in power
systems. With increasing uncertainties and complexity in modern power grids, there is a …