PINNs-Based Uncertainty Quantification for Transient Stability Analysis

• URL: http://arxiv.org/abs/2311.12947v1
• Date: Tue, 21 Nov 2023 19:21:49 GMT
• Title: PINNs-Based Uncertainty Quantification for Transient Stability Analysis
• Authors: Ren Wang, Ming Zhong, Kaidi Xu, Lola Gir\'aldez S\'anchez-Cort\'es, Ignacio de Cominges Guerra
• Abstract summary: We introduce a novel application of Physics-Informed Neural Networks (PINNs), specifically an Ensemble of PINNs (E-PINNs) to estimate critical parameters. E-PINNs capitalize on the underlying physical principles of swing equations to provide a robust solution. The study advances the application of machine learning in power system stability, paving the way for reliable and computationally efficient transient stability analysis.
• Score: 22.116325319900973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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