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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
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    References listed on IDEAS

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    3. 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.
    4. 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.
    5. 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.
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    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.

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