Physics-Informed Neural Networks for Power Systems

Georgios Misyris, Andreas Venzke, Spyros Chatzivasileiadis

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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
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE Power & Energy Society General Meeting
Number of pages5
Publication date2020
Publication statusPublished - 2020
Event2020 IEEE Power and Energy Society General Meeting - Virtual event, Montreal, Canada
Duration: 2 Aug 20206 Aug 2020


Conference2020 IEEE Power and Energy Society General Meeting
LocationVirtual event
Internet address


  • Deep learning
  • Neural network
  • Power system dynamics
  • Power flow
  • System inertia


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