PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks

Jochen Stiasny*, Baosen Zhang, Spyros Chatzivasileiadis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The dynamic behaviour of a power system can be described by a system of differential–algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator – PINNSim – that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.
Original languageEnglish
Article number110796
JournalElectric Power Systems Research
Volume235
Number of pages8
ISSN0378-7796
DOIs
Publication statusPublished - 2024

Keywords

  • Dynamical systems
  • Differential-algebraic equations
  • Physics-informed neural networks
  • Time-domain simulation

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