A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing

Shuangyong Zhuang, Wei Zhao, Qing Wang, Zhe Wang, Lei Chen and Songling Huang
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Shuangyong Zhuang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Wei Zhao: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Qing Wang: Department of Engineering, Durham University, Durham DH1 3LE, UK
Zhe Wang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Lei Chen: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Songling Huang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Energies, 2019, vol. 12, issue 13, 1-19

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.

Keywords: supraharmonic; Bayesian compressive sensing (BCS); multiple measurement vectors (MMV); relevance vector machine (RVM); high-resolution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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