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.
Advancements in Power System Dynamics and Control
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