1. Bayesian Inference of Parameters in Power System Dynamic Models Using Trajectory Sensitivities.
- Author
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Nagi, Rubinder, Huan, Xun, and Chen, Yu Christine
- Subjects
BAYESIAN field theory ,DYNAMIC models ,DYNAMICAL systems ,REACTIVE power ,NUMBER systems ,NONLINEAR systems ,SCALABILITY - Abstract
We propose an analytically tractable Bayesian method to infer parameters in power system dynamic models from noisy measurements of bus-voltage magnitudes and frequencies as well as active- and reactive-power injections. The proposed method is computationally appealing as it bypasses the large number of system model simulations typically required in sampling-based Bayesian inference. Instead, it relies on analytical linearization of the nonlinear system differential-algebraic-equation model enabled by trajectory sensitivities. Central to the proposed method is the construction of a linearized model with the maximum probability of being (closest to) the actual nonlinear model that gave rise to the measurement data. The linear model together with Gaussian prior leads to a conjugate family where the parameter posterior, model evidence, and their gradients can be computed in closed form, markedly improving scalability for large-scale power systems. We illustrate the effectiveness and key features of the proposed method with numerical case studies for a three-bus system. Algorithmic scalability is then demonstrated via case studies involving the New England 39-bus test system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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