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

Author

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  • Wenjian Hu

    (College of Electrical Engineering and Automation, Anhui University, Hefei 232000, China
    Power Quality Engineering Research Center, Ministry of Education, Hefei 232000, China)

  • Mingxing Zhu

    (College of Electrical Engineering and Automation, Anhui University, Hefei 232000, China
    Power Quality Engineering Research Center, Ministry of Education, 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)

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.

Suggested Citation

  • Wenjian Hu & Mingxing Zhu & Huaying Zhang, 2022. "Application of Block Sparse Bayesian Learning in Power Quality Steady-State Data Compression," Energies, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2479-:d:781310
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    References listed on IDEAS

    as
    1. Ngo Minh Khoa & Le Van Dai, 2020. "Detection and Classification of Power Quality Disturbances in Power System Using Modified-Combination between the Stockwell Transform and Decision Tree Methods," Energies, MDPI, vol. 13(14), pages 1-30, July.
    2. Ferhat Ucar & Jose Cordova & Omer F. Alcin & Besir Dandil & Fikret Ata & Reza Arghandeh, 2019. "Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks," Energies, MDPI, vol. 12(8), pages 1-26, April.
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