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

1 Citation (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

Fingerprint

Electric power system measurement
Phasor measurement units
Clustering Analysis
Data compression
Data Compression
Unit
Power System
Spatial Clustering
Multiscale Analysis
Dimensionality Reduction
Principal component analysis
Principal Component Analysis
Matrix Approximation
Wavelet decomposition
Wavelet Decomposition
Risk Factors
Preconditioning
Compression
Segmentation
Voltage

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

Cite this

Lee, Gyul ; Kim, Do In ; Kim, Seon Hyeog ; Shin, Yong June. / Multiscale PMU data compression via density-based WAMS clustering analysis In: Energies. 2019 ; Vol. 12, No. 4.
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Multiscale PMU data compression via density-based WAMS clustering analysis . / Lee, Gyul; Kim, Do In; Kim, Seon Hyeog; Shin, Yong June.

In: Energies, Vol. 12, No. 4, 617, 15.02.2019.

Research output: Contribution to journalArticle

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