Our work during the first 2.5 years of VeriPhIED has revolved around 2 main topics. First, the development of Physics-Informed Neural Networks (PINNs) for power systems. PINNs are unique in the sense that they not only learn from data, but they rather exploit the underlying physical laws to deliver a Neural Network that is more accurate and less dependent on the quality of the training data.
Our team was the first to introduce physics-informed neural networks in the field of power systems. During the project, we worked with the leading offshore wind developer in the world, the Danish company Ørsted. Ørsted is strongly interested in speeding up their simulations as this can accelerate their wind farm design processes and reduce the time to construction. We showed that PINNs can achieve a 100x speedup of these simulations: to be specific, we achieved a computation time of approx. 30 minutes for both training and evaluation of the PINNs, instead of 2 days that conventional tools would have required.
Trying to address the scalability issues we have been facing with Physics-Informed Neural Networks, we put forward a completely new concept: PINNSim, a neural-network based simulator. PINNSim presents a modular and scalable approach to perform power system dynamic simulations with neural networks, at hopefully 10x-100x higher speeds. Each component is modelled by a separate PINN. The novelty comes from the new algorithm that has to integrate all PINNs in a single framework to simulate a whole system. PINNSim is still in a proof-of-concept phase.
Second, we focused on Neural Network verification. Our work has laid the foundation for delivering the performance guarantees of Neural Network that perform classification (e.g. assessing if critical scenarios lead to blackouts or not) or regression (e.g. determining optimal operating points) in power systems. Trying to address scalability issues when treating larger neural networks we exploited the substantial body of work carried out on neural network verification in the field of computer science and the winning open-source algorithm ab-CROWN, which, however, cannot apply in power system problems. Our work so far has tailored the ab-CROWN algorithm so that it can treat successfully power system optimization problems, and it won the best paper award in the HICSS 2025 conference.
Besides Physics-Informed Neural Networks and Neural Network verification, we have worked with Neural Network interpretability, and also developed methods that use neural networks to accelerate power system optimization. Finally, we have also developed an open-source toolbox, with methods that go beyond the state of the art, to efficiently generate data samples for training neural networks for power system stability assessment.
In terms of knowledge transfer and dissemination, we had several activities over the past 2.5 years, with the one standing one probably being the Tutorial Day on “Trustworthy AI for Power Systems”, which we were invited to organize during the 2024 Power System Computation Conference. Distinguished speakers from MIT, GeorgiaTech, Univ. of Washington, Univ. of Vermont, and DTU offered lectures to 200 attendees. All slides and code can be found here:
http://www.chatziva.com/pscc2024.html(se abrirá en una nueva ventana)
