Summary of Enhanced Physics-informed Neural Networks (pinns) For High-order Power Grid Dynamics, by Vineet Jagadeesan Nair
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
by Vineet Jagadeesan Nair
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. The authors propose novel enhancements to improve PINN training and accuracy, building on recent literature. They apply these enhanced PINNs to study the transient dynamics of synchronous generators and make progress towards applying them to advanced inverter models. This work has the potential to accelerate high-fidelity simulations needed for a stable and reliable renewables-rich future grid. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called neural networks to help predict what will happen in power grids that use lots of renewable energy. The authors want to make these predictions more accurate by using ideas from physics, like how magnets work. They test their new method on real-life problems with generators and make progress towards using it for other important things, like predicting how solar panels will perform. This could help us create a better future grid that is reliable and works well. |
