PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids
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Abstract (arXiv)
PowerDynamics.jl is a Julia package for time-domain modeling of power grids that is specifically designed for the stability analysis of systems with high shares of renewable energies. It makes use of Julia's state-of-the-art differential equation solvers and is highly performant even for systems with a large number of components. Further, it is compatible with Julia's machine learning libraries and allows for the utilization of these methods for dynamical optimization and parameter fitting. The package comes with a number of predefined models for synchronous machines, transmission lines and inverter systems. However, the strict open-source approach and a macro-based user-interface also allows for an easy implementation of custom-built models which makes it especially interesting for the design and testing of new control strategies for distributed generation units. This paper presents how the modeling concept, implemented component models and fault scenarios have been experimentally tested against measurements in the microgrid lab of TECNALIA.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2021.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2.
6 papers.
- 2021Predicting Basin Stability of Power Grids using Graph Neural Networkscited by 10
- 2024Projected Neural Differential Equations for Learning Constrained Dynamicscited by 5
- 2022Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learningcited by 4
- 2022Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networkscited by 3
- 2022Towards dynamic stability analysis of sustainable power grids using graph neural networkscited by 2
- 2024Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learningcited by 1
- 2024Projected Neural Differential Equations for Learning Constrained Dynamics31 Oct 2024
- 2024Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning13 Jun 2024
- 2022Towards dynamic stability analysis of sustainable power grids using graph neural networks21 Dec 2022
- 2022Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning17 Oct 2022
- 2022Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks10 Jun 2022
- 2021Predicting Basin Stability of Power Grids using Graph Neural Networks18 Aug 2021