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Keywords

sparse fast Fourier transform; spectral rearrangement; windowed filtering; bucketization-localization; estimation loop

Abstract

As the "dual-carbon" strategy goals are implemented, the oscillations induced by high-penetration power electronic devices are increasingly characterised by the wide frequency domain, strong time-varying and multimodality. The sampling frequency of modern wide-band measurement devices has been substantially increased accordingly to monitor such oscillations. Traditional fast Fourier transform (FFT) parameter identification methods face a contradiction between identification accuracy and identification speed. To this end, a fast identification algorithm for oscillation parameters of multi-modal wide-band signals based on the sparse Fourier transform (SFT) is proposed. Firstly, by utilizing the frequency-domain sparsity of wide-band signals of power systems, multiple non-zero frequency-domain coefficients are hashed into a limited number of buckets to realize computational optimization. Then, multiple iterations are adopted to perform multiple times of spectral rearrangement and localization for wide-band signals. On the premise of maintaining high identification accuracy, the computational efficiency of the identification process is effectively improved. Finally, case studies are employed to verify the proposed algorithm. The results show that the proposed method achieves high precision, fast processing, and improved robustness in identifying multi-modal wide-band signals under the scenarios of high sampling rates.

DOI

10.19781/j.issn.1673-9140.2026.02.028

First Page

314

Last Page

324

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