Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

In order to deal with the problem of massive historical multi-station SCADA data storage in distribution management system, this paper proposes a data compression method for power distribution system based on tensor Tucker decomposition. Firstly, to maintain the high-dimensional spatial structure of multi-station SCADA data of distribution management system in the presentation stage, we establish a third-order tensor representation model of multi-station SCADA data. Then, a historical SCADA data compression method for distribution management system based on tensor tucker decomposition is proposed. This method can compress the data while maintaining the spatial intrinsic structure of multi-station SCADA data. Finally, the effectiveness of the method is verified using real data. And, the results of comparison with singular value decomposition method and principal component analysis method show that the proposed method is superior to the traditional method.

[1]  Andreas Unterweger,et al.  Resumable Load Data Compression in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[2]  Ma Fayon The Research of Historical Data Compression and Storage Strategy in Power Dispatch SCADA System , 2014 .

[3]  Caiming Zhang,et al.  Image Denoising Based on HOSVD With Iterative-Based Adaptive Hard Threshold Coefficient Shrinkage , 2019, IEEE Access.

[4]  Wing-Hong Lau,et al.  Real-Time Power-Quality Monitoring With Hybrid Sinusoidal and Lifting Wavelet Compression Algorithm , 2012, IEEE Transactions on Power Delivery.

[5]  Daoqu Geng,et al.  Big Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform , 2019, IEEE Access.

[6]  Sarasij Das,et al.  Principal component analysis based compression scheme for power system steady state operational data , 2011, ISGT2011-India.

[7]  M. Ringwelski,et al.  The Hitchhiker's guide to choosing the compression algorithm for your smart meter data , 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

[8]  Joos Vandewalle,et al.  On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors , 2000, SIAM J. Matrix Anal. Appl..

[9]  Fang Zhang,et al.  Application of a real-time data compression and adapted protocol technique for WAMS , 2015, 2015 IEEE Power & Energy Society General Meeting.

[10]  Reinhold Schneider,et al.  The Alternating Linear Scheme for Tensor Optimization in the Tensor Train Format , 2012, SIAM J. Sci. Comput..

[11]  Cong Liu,et al.  A Wavelet-Based Data Compression Technique for Smart Grid , 2011, IEEE Transactions on Smart Grid.

[12]  Laurence T. Yang,et al.  A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction , 2014, IEEE Transactions on Emerging Topics in Computing.

[13]  Rasmus Bro,et al.  Improving the speed of multi-way algorithms:: Part I. Tucker3 , 1998 .

[14]  Taskin Koçak,et al.  A Survey on Smart Grid Potential Applications and Communication Requirements , 2013, IEEE Transactions on Industrial Informatics.

[15]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[16]  Tae-Ro Lee,et al.  Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System , 2017 .

[17]  Jiliu Zhou,et al.  Using Tucker Decomposition to Compress Color Images , 2009, 2009 2nd International Congress on Image and Signal Processing.

[18]  Julio Cesar Stacchini de Souza,et al.  Data Compression in Smart Distribution Systems via Singular Value Decomposition , 2017, IEEE Transactions on Smart Grid.

[19]  Siep Weiland,et al.  A tensor decomposition approach to data compression and approximation of ND systems , 2012, Multidimens. Syst. Signal Process..