Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Gyul Lee, Do-In Kim, Seon Hyeog Kim and Yong-June Shin
Additional contact information
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

Energies, 2019, vol. 12, issue 4, 1-17

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.

Keywords: phasor measurement unit (PMU); data compression; density-based clustering; MSPCA (multiscale principal component analysis); wide-area power systems (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/4/617/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/4/617/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:4:p:617-:d:206207

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().