Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …

Smart grid is confronted with cyberattacks due to the increasing dependence on cyberspace.
False data injection attacks (FDIAs) represent a major type of cyberattacks that cannot be …

This paper proposes novel architectures for spatio-temporal graph convolutional and
recurrent neural networks whose structure is inspired by the physics of power systems. The …

The effective representation, processing, analysis, and visualization of large-scale structured
data over graphs, especially power grids, are gaining a lot of attention. So far most of the …

Missing values in wastewater treatment process (WWTP) data hinder the monitoring and
prediction of operational status. Therefore, various online imputation methods have been …

Robust attack detection is critical for ensuring the reliability and security of power systems,
which are increasingly vulnerable to sophisticated cyber–physical disruptions. Traditional …

Denial-of-service (DoS) attacks adversely impact the state estimation (SE) techniques used
in power systems. Our contributions in this article are twofold. First, considering a longer …

In an industrial control system, a programmable logic controller (PLC) plays a vital role in
maintaining the stable operation of the system. Cyber-attacks can affect the regular …

The availability of high-fidelity time-series data is essential for distribution grid operations
such as state estimation, prediction, protection, and scheduling of distributed energy …

This article proposes the application of correlated Gaussian processes (Corr-GPs) for the
recovery of missing intervals in power systems signals. Based on only local power system …