Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition

Sandoval Guzmán, Betsy; Barocio, Emilio; Korba, Petr; Obushevs, Artjoms; Segundo Sevilla, Felix Rafael

doi:10.1016/j.segan.2022.100917

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29852

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition
Authors:	Sandoval Guzmán, Betsy Barocio, Emilio Korba, Petr Obushevs, Artjoms Segundo Sevilla, Felix Rafael
et. al:	No
DOI:	10.1016/j.segan.2022.100917 10.21256/zhaw-29852
Published in:	Sustainable Energy, Grids and Networks
Volume(Issue):	32
Issue:	100917
Issue Date:	2022
Publisher / Ed. Institution:	Elsevier
ISSN:	2352-4677
Language:	English
Subjects:	Data compression; Tucker decomposition; Tensor decomposition; Smart meter; Phasor Measurement Unit (PMU); Missing data; Clustering; Power system
Subject (DDC):	005: Computer programming, programs and data 621.3: Electrical, communications, control engineering
Abstract:	In this paper, an innovative data compression methodology based on Tucker tensor decomposition is presented. The proposed approach exploits the benefits of organizing time series in multidimensional arrays (tensors) in order to achieve higher compression ratios of the original data while preserving most of their properties and, in this form, achieving a low reconstruction error. This reorganization of the data into the tensor allows the creation of correlation for heterogeneous data. Furthermore, it reduces the direct relationship between the number of columns and rows with the final size of the compressed file when the data is compressed using algorithms based on matrix-based dimensional reduction techniques, such as the Singular Value Decomposition (SVD). At the same time, the problem of missing data can be overcome since the problem is formulated as an iterative process. To demonstrate the effectiveness of the innovative algorithm proposed, two study cases from electrical networks are presented. First, the compression of real synchrophasor data acquired from one commercial Phasor Measurement Unit (PMU) measuring 3-phase voltages and frequency measurements from the electrical system of Continental Europe is presented, where a significant CR is achieved for apparently no correlated data. Then, an application to real Smart Meter measurements from the electrical utility ERCOT in the USA is also provided. Here the clustering of the data is obtained as an additional outcome of the proposed algorithm. To underline the benefits of the innovative proposed methodology in contrast to other more common decompositions such as SVD, the compression ratios and reconstruction error achieved using these two different techniques are compared. The results indicate that using Tucker decomposition, not only higher compression can be achieved, but also it is demonstrated that for SM data, additional attributes can be obtained, such as clustering.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29852
Fulltext version:	Accepted version
License (according to publishing contract):	CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Restricted until:	2024-09-16
Departement:	School of Engineering
Organisational Unit:	Institute of Energy Systems and Fluid Engineering (IEFE)
Appears in collections:	Publikationen School of Engineering

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Sandoval Guzmán, B., Barocio, E., Korba, P., Obushevs, A., & Segundo Sevilla, F. R. (2022). Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition. Sustainable Energy, Grids and Networks, 32(100917). https://doi.org/10.1016/j.segan.2022.100917

Sandoval Guzmán, B. et al. (2022) ‘Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition’, Sustainable Energy, Grids and Networks, 32(100917). Available at: https://doi.org/10.1016/j.segan.2022.100917.

B. Sandoval Guzmán, E. Barocio, P. Korba, A. Obushevs, and F. R. Segundo Sevilla, “Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition,” Sustainable Energy, Grids and Networks, vol. 32, no. 100917, 2022, doi: 10.1016/j.segan.2022.100917.

SANDOVAL GUZMÁN, Betsy, Emilio BAROCIO, Petr KORBA, Artjoms OBUSHEVS und Felix Rafael SEGUNDO SEVILLA, 2022. Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition. Sustainable Energy, Grids and Networks. 2022. Bd. 32, Nr. 100917. DOI 10.1016/j.segan.2022.100917

Sandoval Guzmán, Betsy, Emilio Barocio, Petr Korba, Artjoms Obushevs, and Felix Rafael Segundo Sevilla. 2022. “Data Compression for Advanced Monitoring Infrastructure Information in Power Systems Based on Tensor Decomposition.” Sustainable Energy, Grids and Networks 32 (100917). https://doi.org/10.1016/j.segan.2022.100917.

Sandoval Guzmán, Betsy, et al. “Data Compression for Advanced Monitoring Infrastructure Information in Power Systems Based on Tensor Decomposition.” Sustainable Energy, Grids and Networks, vol. 32, no. 100917, 2022, https://doi.org/10.1016/j.segan.2022.100917.