Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Energies

Do-In Kim¹  Yong-June Shin¹  Gyul Lee¹  SeonHyeog Kim¹ 

[1] Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea;

关键词: phasor measurement unit (PMU);    data compression;    density-based clustering;    MSPCA (multiscale principal component analysis);    wide-area power systems;   

DOI  :  10.3390/en12040617

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown