Physics-Informed Neural Networks for Power Systems

By George S. Misyris, Andreas Venzke, Spyros Chatzivasileiadis. IEEE Power & Energy Society General Meeting, 2019

327 citations recorded by the source corpus.

This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, this paper proposes a neural network training procedure that can make use of the wide range of mathematical models describing power system behavior, both in steady-state and in dynamics. Physics-informed neural networks require substantially less training data and can result in simpler neural network structures, while achieving high accuracy. This work unlocks a range of opportunities in power systems, being able to determine dynamic states, such as rotor angles and frequency, and uncertain parameters such as inertia and damping at a fraction of the computational time required by conventional methods. This paper focuses on introducing the framework and showcases its potential using a single-machine infinite bus system as a guiding example. Physics-informed neural networks are shown to accurately determine rotor angle and frequency up to 87 times faster than conventional methods.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fe4d1aaa0369a506fd168ad2d161aaba6730080e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gianluigi Rozza
2. Francesco Piccialli
3. Fabio Giampaolo
4. Salvatore Cuomo
5. Vincenzo Schiano Di Cola
6. Maizar Raissi
7. G. Em Karniadakis
8. Paris Perdikaris

In Electrical and Electronic Engineering

1. Maizar Raissi
2. Gianluigi Rozza
3. G. Em Karniadakis
4. Jan S. Hesthaven
5. Dongkun Zhang

In Statistical and Nonlinear Physics

1. Gianluigi Rozza
2. Fabio Giampaolo
3. Salvatore Cuomo
4. Francesco Piccialli
5. Vincenzo Schiano Di Cola
6. Andrea Manzoni
7. Maizar Raissi
8. G. Em Karniadakis

Citations in the corpus

• Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, methodology): “A PINN was also developed to address power system applications [114] by solving the swing equation, which has been simplified to an ODE.”