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Spatial and temporal PMU data compression for efficient data archiving in modern control centres

: Kummerow, Andre; Nicolai, Steffen; Bretschneider, Peter


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Energy Conference, ENERGYCON 2018 : 3-7 June 2018, Limassol, Cyprus
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-3669-5
ISBN: 978-1-5386-1283-5
International Energy Conference (ENERGYCON) <2018, Limassol>
Conference Paper
Fraunhofer IOSB ()

The scope of this survey is the compression of PMU data by eliminating redundant information with statistical methods. With that the storage demand in wide area monitoring systems for archiving massive phasor data can be significantly reduced and enables the efficient integration of PMU based applications in modern control centres. Within this paper a general concept is introduced for data reduction in space and time dimension combining different techniques from dimensionality reduction. For this purpose several unsupervised statistical learning methods (e.g. PCA, ICA, NMF) are tested using a real PMU measurement dataset.