The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

State estimation (SE) is indispensable for the situational awareness of power systems.
Conventional SE is fed by measurements collected from the supervisory control and data …

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …

The development of machine learning (ML) provides a promising solution to guarantee the
structural integrity of critical components during service period. However, considering the …

Contemporary power grids are being challenged by rapid and sizeable voltage fluctuations
that are caused by large-scale deployment of renewable generators, electric vehicles, and …

We develop, in this paper, a machine learning approach to optimize the real-time operation
of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow …

State estimation is highly critical for accurately observing the dynamic behavior of the power
grids and minimizing risks from cyber threats. However, existing state estimation methods …

Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …