Shuangyong Zhuang
A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing
Zhuang, Shuangyong; Zhao, Wei; Wang, Qing; Wang, Zhe; Chen, Lei; Huang, Songling
Authors
Wei Zhao
Dr Qing Wang qing.wang@durham.ac.uk
Associate Professor
Dr Qing Wang qing.wang@durham.ac.uk
Associate Professor
Lei Chen
Songling Huang
Abstract
Supraharmonics emitted by electrical equipment have caused a series of electromagnetic interference in power systems. Conventional supraharmonic analysis algorithms, e.g., discrete Fourier transform (DFT), have a relatively low frequency resolution with a given observation time. Our previous work supplied a significant improvement on the frequency resolution based on multiple measurement vectors and orthogonal matching pursuit (MMV-OMP). In this paper, an improved algorithm for supraharmonic analysis, which employs Bayesian compressive sensing (BCS) for further improving the frequency resolution, is proposed. The performance of the proposed algorithm on the simulation signal and experimental data show that the frequency resolution can be improved by about a magnitude compared to that of the MMV-OMP algorithm, and the signal frequency estimation error is about 20 times better. In order to identify the signals in two adjacent frequency grids with one resolution, a normalized inner product criterion is proposed and verified by simulations. The proposed algorithm shows a potential for high-accuracy supraharmonic analysis
Citation
Zhuang, S., Zhao, W., Wang, Q., Wang, Z., Chen, L., & Huang, S. (2019). A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing. Energies, 12(13), Article 2559. https://doi.org/10.3390/en12132559
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 28, 2019 |
Online Publication Date | Jul 3, 2019 |
Publication Date | Jul 3, 2019 |
Deposit Date | Jul 12, 2019 |
Publicly Available Date | Jul 12, 2019 |
Journal | Energies |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 13 |
Article Number | 2559 |
DOI | https://doi.org/10.3390/en12132559 |
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