Toolbox for Developing Physics Informed Neural Networks for Power Systems Components

This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing complexity of modern power systems, necessitating faster and more scalable alternatives. Physics-Informed Neural Networks (PINNs) show great potential for rapidly solving the ordinary differential equations (ODEs) that govern power system dynamics. This is vital for reliability studies, cost optimizations, and real-time decision-making in the electricity grid. Despite their potential, standardized frameworks for training PINNs remain scarce. This poses a barrier for the broader adoption and reproducibility of PINNs; it also does not allow the streamlined creation of a PINN-based model library. This paper addresses these gaps. It introduces a Python-based toolbox for developing PINNs tailored to power system components, available on GitHub https://github.com/radiakos/PowerPINN. Using this framework, we capture the dynamic characteristics of a 9th-order system, which, to the best of our knowledge, is the most complex power system component trained with a PINN to date, demonstrating the toolbox’s capabilities, limitations, and potential improvements. The toolbox is open and free to use by anyone interested in creating PINN-based models for power system components.