Parameter Identification for Power System Oscillations via Bayesian Inference of Synchrophasor Data

This article presents a novel Bayesian inference method based on phasor measurement unit (PMU) data for identifying power system oscillation parameters. By leveraging synchronized data, the proposed method captures the dynamic behavior of the power system in real time. The approach integrates variational mode decomposition (VMD) and fast Fourier transform (FFT) to efficiently separate and identify oscillation modes, which enhances the initial setup for Bayesian inference. Using no-u-turn sampler (NUTS) sampling, the method estimates the modal parameters, including amplitude, damping factor, and frequency, while quantifying their uncertainties. Validations with both simulated and real power system data demonstrate that the proposed method effectively identifies oscillation parameters with high accuracy. This work provides a robust tool for the real-time monitoring and dynamic analysis of power system oscillations, offering improvements in accuracy and responsiveness over traditional methods.