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Propagating Uncertainty in Power System Initial Conditions Using Data-Driven Linear Operators
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DOI：10.1109/TPWRS.2022.3182570.png)
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摘要
En 中文
In this paper, we propose a data-driven approach for uncertainty moment propagation through the non-linear power system dynamics. The proposed approach relies on the linear representation of a nonlinear system using the Perron-Frobenius & Koopman operators for propagating the moments. Data from non-linear simulations is used to approximate the linear operators by matrices, thereby enabling moment propagation by matrix multiplication. Results for a large-scale system are presented to demonstrate the accuracy and speed-up of the proposed method.
Keyword:
Uncertainty
Power system dynamics
Analytical models
Time-domain analysis
Hypercubes
Computational modeling
Trajectory
Linear operators
Perron-Frobenius operator
Koopman operator
uncertainty propagation
moment propa- gation
data-driven methods
uncertainty quantification
