Application of Block Sparse Bayesian Learning in Power Quality Steady-State Data Compression

Wenjian Hu, Mingxing Zhu and Huaying Zhang
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Wenjian Hu: College of Electrical Engineering and Automation, Anhui University, Hefei 232000, China
Mingxing Zhu: College of Electrical Engineering and Automation, Anhui University, Hefei 232000, China
Huaying Zhang: New Smart City High-Quality Power Supply Joint Laboratory of China Southern Power Grid, Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China

Energies, 2022, vol. 15, issue 7, 1-17

Abstract: In modern power systems, condition monitoring equipment generates a great deal of steady-state data that are too large for data transmission and, thus, data compression is needed. Therefore, there is a balance to strike between compression quality and data accuracy. Greedy algorithms are effective but suffer from low data reconstruction accuracy. This paper proposes a block sparse Bayesian learning (BSBL)-based data compression method. Based on the prior distribution and posterior probability of the sparse signals, it uses the Bayesian formula to excavate the block structure of these signals. This paper also adds two indicators to the evaluation process to validate the proposed method. The proposed method is effective in terms of signal-to-noise ratio (SNR), relative root mean square error (RRMSE), amplitude error, energy recovery percentage (ERP), and angle error. The first three indicate better performance of the proposed method than the traditional method by giving the same compression ratio. Therefore, the method validates the possibility of a more accurate and economical solution to power quality assurance.

Keywords: block sparse Bayesian learning; compressed sensing; data compression; evaluation indicators; power quality steady-state data (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: 2022
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