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Found 5 papers, 2 papers with code
Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid
We substantially reduce the complexity of the dynamics by tuning the system to align with a reduced swing-equation model.
Projected Neural Differential Equations for Learning Constrained Dynamics
Neural differential equations offer a powerful approach for learning dynamics from data.
Complex-Phase, Data-Driven Identification of Grid-Forming Inverter Dynamics
The increasing integration of renewable energy sources (RESs) into power systems requires the deployment of grid-forming inverters to ensure a stable operation.
Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Importantly, we demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids.
An Open Source Software Stack for Tuning the Dynamical Behavior of Complex Power Systems
BlockSystems. jl and NetworkDynamics. jl are two novel software packages which facilitate highly efficient transient stability simulations of power networks.
