Next Article in Journal
Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools
Next Article in Special Issue
Analysis of IEC 61850-9-2LE Measured Values Using a Neural Network
Previous Article in Journal
Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm
Previous Article in Special Issue
Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region
Article

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Proceedings of IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, 23–27 October 2017.
Received: 21 January 2019 / Revised: 10 February 2019 / Accepted: 11 February 2019 / Published: 15 February 2019
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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. View Full-Text
Keywords: phasor measurement unit (PMU); data compression; density-based clustering; MSPCA (multiscale principal component analysis); wide-area power systems phasor measurement unit (PMU); data compression; density-based clustering; MSPCA (multiscale principal component analysis); wide-area power systems
Show Figures

Graphical abstract

MDPI and ACS Style

Lee, G.; Kim, D.-I.; Kim, S.H.; Shin, Y.-J. Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis. Energies 2019, 12, 617. https://0-doi-org.brum.beds.ac.uk/10.3390/en12040617

AMA Style

Lee G, Kim D-I, Kim SH, Shin Y-J. Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis. Energies. 2019; 12(4):617. https://0-doi-org.brum.beds.ac.uk/10.3390/en12040617

Chicago/Turabian Style

Lee, Gyul, Do-In Kim, Seon H. Kim, and Yong-June Shin. 2019. "Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis" Energies 12, no. 4: 617. https://0-doi-org.brum.beds.ac.uk/10.3390/en12040617

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop