A Real-time Synchrophasor Data Compression Method Using Singular Value
Decomposition
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Reza Pourramezan , Reza Hassani , Houshang Karimi , Mario Paolone , Jean Mahseredjian
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Electrical Engineering, Polytechnique Montreal, Distributed Electrical Systems Laboratory, EPFL
The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large
amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a
singular value decomposition (SVD)-based method for compression of synchrophasor data, including magnitude,
phase-angle, and complex phasor. The proposed method includes a dimensionality evaluation and reduction
technique and a real-time progressive partitioning algorithm. The proposed dimensionality reduction technique
employs the measurement uncertainty of PMUs and introduces a threshold criterion on the signal-to-noise ratio (SNR)
of SVD modes. Singular modes with high SNR are retained, and those dominated by measurement error are
discarded to achieve a high compression ratio (CR) while preserving the critical information with adequate accuracy.
The proposed progressive partitioning separates the data corresponding to normal and disturbance conditions by
monitoring the dimensionality variations in real-time. The partitions containing the data of similar dimensionality are
separately compressed to further improve the accuracy and CR. The performance of the proposed method is
evaluated and benchmarked against state-of-the-art methods using both field and simulated PMU data. The results
show that the proposed method provides high CR while accurately preserving the critical information of events and
disturbances.
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