IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2479-d781310.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2479/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2479/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mario Šipoš & Zvonimir Klaić & Emmanuel Karlo Nyarko & Krešimir Fekete, 2021. "Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm," Energies, MDPI, vol. 14(1), pages 1-13, January.
    2. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
    3. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.
    4. Julio Barros, 2022. "New Power Quality Measurement Techniques and Indices in DC and AC Networks," Energies, MDPI, vol. 15(23), pages 1-3, December.
    5. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    6. Ali Riza Ekti & Aaron Wilson & Joseph Olatt & John Holliman & Serhan Yarkan & Peter Fuhr, 2022. "A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance," Energies, MDPI, vol. 15(22), pages 1-12, November.
    7. Xu Han & Dujie Hou & Xiong Cheng & Yan Li & Congkai Niu & Shuosi Chen, 2022. "Prediction of TOC in Lishui–Jiaojiang Sag Using Geochemical Analysis, Well Logs, and Machine Learning," Energies, MDPI, vol. 15(24), pages 1-25, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2479-:d:781310. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.