Synchrophasor-Based Parameter Identification of Sub/Super-Synchronous Oscillations Using Adaptive Empirical Fourier Decomposition

Synchrophasor-based sub-synchronous oscillation (SSO) parameter identification is effective for monitoring SSOs, while its performance can be significantly affected by measurement noise, posing serious challenges to reliable identification. This study proposes an improved identification scheme that combines adaptive empirical Fourier decomposition (AEFD) with the Prony method to enable accurate and simultaneous estimation of sub/super-synchronous modes. First, the AEFD method is applied to oscillation signals, effectively decomposing the signals into sub-synchronous and super-synchronous modal components. Particularly, the sparsity index (SI) is introduced to determine the number of oscillation modes contained in the signal. Subsequently, the Prony method is employed on the decomposed components to extract modal parameters. The proposed method effectively suppresses modal aliasing and improves noise robustness of the empirical wavelet transform by employing an improved spectrum segmentation technique and a zero-phase filter bank, thereby enhancing the extraction accuracy of the estimation results. Through comparisons with existing methods and simulated case studies, it is verified that the proposed method performs exceptionally well in terms of accuracy, mode mixing suppression and noise robustness, demonstrating its superiority and effectiveness in the extraction of sub/super-synchronous oscillation parameters.