# PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids

arXiv: https://arxiv.org/abs/2101.02103 · HTML: https://mlsanity.com/paper/2101.02103/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 6 of 6)

- [Predicting Basin Stability of Power Grids using Graph Neural Networks](https://mlsanity.com/paper/2108.08230/) (2021)
- [Projected Neural Differential Equations for Learning Constrained Dynamics](https://mlsanity.com/paper/2410.23667/) (2024)
- [Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks](https://mlsanity.com/paper/2206.06369/) (2022)
- [Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning](https://mlsanity.com/paper/2210.09266/) (2022)
- [Towards dynamic stability analysis of sustainable power grids using graph neural networks](https://mlsanity.com/paper/2212.11130/) (2022)
- [Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning](https://mlsanity.com/paper/2406.08917/) (2024)

## 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.