Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems

Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


This paper introduces for the first time, to the best of our knowledge, the Bayesian Physics-Informed Neural Networks for applications in power systems. Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output. Such a confidence measure can be very valuable for the operation of safety critical systems, such as power systems, as it offers a degree of 'trustworthiness' for the neural network output. This paper applies the BPINNs for robust identification of the system inertia and damping, using a single machine infinite bus system as the guiding example. The goal of this paper is to introduce the concept and explore the strengths and weaknesses of BPINNs compared to existing methods. We compare BPINNs with the PINNs and the recently popular method for system identification, SINDy. We find that BPINNs and PINNs are robust against all noise levels, delivering estimates of the system inertia and damping with significantly lower error compared to SINDy, especially as the noise levels increases.
Original languageEnglish
Title of host publicationProceedings of 2023 IEEE Belgrade PowerTech
Number of pages6
Publication date2023
ISBN (Electronic)978-1-6654-8778-8
Publication statusPublished - 2023
Event2023 IEEE Belgrade PowerTech - Hotel Crowne Plaza, Belgrade, Serbia
Duration: 25 Jun 202329 Jun 2023
Conference number: 15


Conference2023 IEEE Belgrade PowerTech
LocationHotel Crowne Plaza


  • Bayesian physics-informed neural networks
  • System identification
  • Swing equation
  • Power system dynamics


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