Advancements in Power System Dynamics and Control

The field of power system dynamics and control is witnessing significant advancements, driven by the increasing share of renewable energy sources and the need for more efficient and robust control methods. Researchers are exploring new approaches to modeling and analyzing power systems, including the use of complex phase analysis, artificial neural networks, and topology-aware graph neural networks. These innovations aim to improve the accuracy and scalability of power flow estimation, grid impedance estimation, and stability analysis. Noteworthy papers in this area include the proposal of a Matlab-based toolbox for automatic EMT modeling and small-signal stability analysis, and the development of a novel adaptive gain-scheduling control scheme for Virtual Synchronous Generators. Another significant contribution is the introduction of a gated graph neural network surrogate for AC power-flow estimation, which demonstrates improved performance and generalization capabilities compared to existing methods.

Sources

Complex Phase Analysis of Power Grid Dynamics

A Matlab-based Toolbox for Automatic EMT Modeling and Small-Signal Stability Analysis of Modern Power Systems

Extreme Scenario Characterization for High Renewable Energy Penetrated Power Systems over Long Time Scales

Revisiting Z Transform Laplace Inversion: To Correct flaws in Signal and System Theory

ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions

Power Flow Analysis of a 5-Bus Power System Based on Newton-Raphson Method

Power-Gas Infrastructure Planning under Weather-induced Supply and Demand Uncertainties

A Bidirectional Power Router for Traceable Multi-energy Management

Reliability Assessment of Power System Based on the Dichotomy Method

Isogeometric contact analysis in subsea umbilical and power cables

Getting Dynamic Line Ratings into Markets

Synchronising DER inverters to weak grid using Kalman filter and LQR current controller

Frequency Domain Design of a Reset-Based Filter: An Add-On Nonlinear Filter for Industrial Motion Control

Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware

The Bias of Subspace-based Data-Driven Predictive Control

Built with on top of