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Preprint
PINNs-Based Uncertainty Quantification for Transient Stability Analysis
arXiv.org
21 Nov 2023
Abstract
This paper addresses the challenge of transient stability in power systems
with missing parameters and uncertainty propagation in swing equations. We
introduce a novel application of Physics-Informed Neural Networks (PINNs),
specifically an Ensemble of PINNs (E-PINNs), to estimate critical parameters
like rotor angle and inertia coefficient with enhanced accuracy and reduced
computational load. E-PINNs capitalize on the underlying physical principles of
swing equations to provide a robust solution. Our approach not only facilitates
efficient parameter estimation but also quantifies uncertainties, delivering
probabilistic insights into the system behavior. The efficacy of E-PINNs is
demonstrated through the analysis of $1$-bus and $2$-bus systems, highlighting
the model's ability to handle parameter variability and data scarcity. The
study advances the application of machine learning in power system stability,
paving the way for reliable and computationally efficient transient stability
analysis.
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Details
- Title
- PINNs-Based Uncertainty Quantification for Transient Stability Analysis
- Creators
- Ren WangMing ZhongKaidi XuLola Giráldez Sánchez-CortésIgnacio de Cominges Guerra
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021871336204721