Options
Bayesian physics-informed neural networks for robust system identification of power systems
Publikationstyp
Conference Paper
Date Issued
2023-08-09
Sprache
English
Citation
IEEE Belgrade PowerTech (2023)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISBN
9781665487788
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.
Subjects
Bayesian Physics-Informed Neural Networks
Power System Dynamics
Swing Equation
System Identification
MLE@TUHH
DDC Class
621: Applied Physics