แนวคิดหลัก
BPINNs outperform SINDy and PINN in system identification under IBR uncertainties.
บทคัดย่อ
This article explores the performance of Bayesian Physics-informed Neural Networks (BPINNs) in identifying power system dynamics under uncertainty from Inverter-based Resources (IBRs). The study compares BPINNs with conventional methods like SINDy and PINN across various power system models. Key highlights include:
Introduction to the importance of accurate system identification in power systems.
Comparison of BPINNs, SINDy, and PINN in handling uncertainties from IBRs.
Evaluation of BPINN performance on different grid systems.
Exploration of transfer learning to reduce training iterations and data requirements.
Analysis of the influence of sampling frequency and collocation points on estimation accuracy.
สถิติ
The BPINN achieves lower errors than SINDy by a factor of 10 to 90 under IBR uncertainties.
คำพูด
"In presence of uncertainty, the BPINN achieves orders of magnitude lower errors than SINDy."
"Transfer learning helps reduce training time by up to 75% for estimation on the 118-bus system."