Multiscale PMU data compression via density-based WAMS clustering analysis

Gyul Lee, Do In Kim, Seon Hyeog Kim, Yong June Shin

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Article number617
JournalEnergies
Volume12
Issue number4
DOIs
Publication statusPublished - 2019 Feb 15

Bibliographical note

Funding Information:
This work was supported by Korea Electric Power Corporation (KEPCO) #CX72170123 and #R18XA05. KEPCO provided technical advices on application of the proposed technique to real-world data.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multiscale PMU data compression via density-based WAMS clustering analysis <sup>†</sup>'. Together they form a unique fingerprint.

Cite this