Fast inverter control is a desideratum towards the smoother integration of renewables.
Adjusting inverter injection setpoints for distributed energy resources can be an effective grid …

The dynamic response of power grids to small events or persistent stochastic disturbances
influences their stable operation. Low-frequency inter-area oscillations are of particular …

This paper presents a physics-inspired graph-structured kernel designed for power flow
learning using Gaussian Process (GP). The kernel, named the vertex-degree kernel (VDK) …

Synchronized data provide unprecedented opportunities for inferring voltage frequencies
and rates of change of frequencies (ROCOFs) across the buses of a power system. Aligned …

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor
measurements for power system monitoring and control. The frequent data quality issues …

Wide-area dynamic studies are of paramount importance to ensure the stability and
reliability of power grids. This paper puts forth a comprehensive framework for inferring the …

Gaussian process (GP) is a powerful modeling method with applications in machine
learning for various engineering and non-engineering fields. Despite numerous benefits of …

The dynamics of power grids are governed by a large number of nonlinear differential and
algebraic equations (DAEs). To safely operate the system, operators need to check that the …

Resumen en español Las redes de distribución activas presentan alta penetración de
recursos distribuidos que requieren supervisión en tiempo real. Estas redes están …

The proliferation of renewables, electric vehicles, and power electronic devices calls for
innovative approaches to learn, optimize, and plan the power system. The uncertain and …