IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i4p617-d206207.html
   My bibliography  Save this article

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Author

Listed:
  • Gyul Lee

    (Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea)

  • Do-In Kim

    (Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea)

  • Seon Hyeog Kim

    (Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea)

  • Yong-June Shin

    (Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea)

Abstract

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

Suggested Citation

  • Gyul Lee & Do-In Kim & Seon Hyeog Kim & Yong-June Shin, 2019. "Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis," Energies, MDPI, vol. 12(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:617-:d:206207
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/4/617/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/4/617/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ngo Minh Khoa & Doan Duc Tung, 2018. "Locating Fault on Transmission Line with Static Var Compensator Based on Phasor Measurement Unit," Energies, MDPI, vol. 11(9), pages 1-14, September.
    2. Igor Ivanković & Igor Kuzle & Ninoslav Holjevac, 2018. "Algorithm for Fast and Efficient Detection and Reaction to Angle Instability Conditions Using Phasor Measurement Unit Data," Energies, MDPI, vol. 11(3), pages 1-21, March.
    3. Aaron Esparza & Juan Segundo & Ciro Nuñez & Nancy Visairo & Emilio Barocio & Héctor García, 2018. "Transient Stability Enhancement Using a Wide-Area Controlled SVC: An HIL Validation Approach," Energies, MDPI, vol. 11(7), pages 1-21, June.
    4. Yi Tang & Feng Li & Chenyi Zheng & Qi Wang & Yingjun Wu, 2018. "PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding," Energies, MDPI, vol. 11(1), pages 1-15, January.
    5. Mario Klarić & Igor Kuzle & Ninoslav Holjevac, 2018. "Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining," Energies, MDPI, vol. 11(12), pages 1-23, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mengting Yao & Yun Zhu & Junjie Li & Hua Wei & Penghui He, 2019. "Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree," Energies, MDPI, vol. 12(13), pages 1-14, June.
    2. Xianggen Yin & Yikai Wang & Jian Qiao & Wen Xu & Xin Yin & Lin Jiang & Wei Xi, 2021. "Multi-Information Fusion-Based Hierarchical Power Generation-Side Protection System," Energies, MDPI, vol. 14(2), pages 1-19, January.

    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. Ziad M. Ali & Seyed-Ehsan Razavi & Mohammad Sadegh Javadi & Foad H. Gandoman & Shady H.E. Abdel Aleem, 2018. "Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus," Energies, MDPI, vol. 11(6), pages 1-17, June.
    2. Arash Abyaz & Habib Panahi & Reza Zamani & Hassan Haes Alhelou & Pierluigi Siano & Miadreza Shafie-khah & Mimmo Parente, 2019. "An Effective Passive Islanding Detection Algorithm for Distributed Generations," Energies, MDPI, vol. 12(16), pages 1-19, August.
    3. Goran Petrovic & Juraj Alojzije Bosnic & Goran Majic & Marin Despalatovic, 2019. "A Design of PWM Controlled Calibrator of Non-Sinusoidal Voltage Waveforms," Energies, MDPI, vol. 12(10), pages 1-14, May.
    4. Zoran Zbunjak & Igor Kuzle, 2019. "System Integrity Protection Scheme (SIPS) Development and an Optimal Bus-Splitting Scheme Supported by Phasor Measurement Units (PMUs)," Energies, MDPI, vol. 12(17), pages 1-21, September.
    5. Andrey Pazderin & Inga Zicmane & Mihail Senyuk & Pavel Gubin & Ilya Polyakov & Nikita Mukhlynin & Murodbek Safaraliev & Firuz Kamalov, 2023. "Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-43, August.
    6. Thomas I. Strasser & Sebastian Rohjans & Graeme M. Burt, 2019. "Methods and Concepts for Designing and Validating Smart Grid Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.
    7. Mario Klarić & Igor Kuzle & Ninoslav Holjevac, 2018. "Wind Power Monitoring and Control Based on Synchrophasor Measurement Data Mining," Energies, MDPI, vol. 11(12), pages 1-23, December.
    8. Panos Kotsampopoulos & Pavlos Georgilakis & Dimitris T. Lagos & Vasilis Kleftakis & Nikos Hatziargyriou, 2019. "FACTS Providing Grid Services: Applications and Testing," Energies, MDPI, vol. 12(13), pages 1-23, July.
    9. Amirreza Mehri & Kazem Mazlumi & Hamed HashemiDezaki & Mohammad Hasan Mansouri & Ramin Mahyaei, 2023. "A Novel Approach for Elimination of Defects of Blocking and Unblocking in Distance Relays during Power Swing," Sustainability, MDPI, vol. 15(18), pages 1-21, September.

    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:12:y:2019:i:4:p:617-:d:206207. 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.