Big Data Compression of Smart Distribution Systems Based on Tensor Tucker Decomposition

被引：0
|
作者：
Zhao H. [1 ]
Ma L. [1 ]
机构：
[1] Hebei Key Laboratory of Distributed Energy Storage and Micro-grid, North China Electric Power University, Baoding, 071003, Hebei Province
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 16期
关键词：
Big data compression; Heterogeneous data standardization; Smart distribution systems; Spatial eigenstructure; Tensor Tucker decomposition;
D O I：
10.13334/j.0258-8013.pcsee.181447
中图分类号：
学科分类号：
摘要：
In order to solve the storage problem caused by massive heterogeneous data in the smart distribution systems, this paper proposed a data compression method based on tensor Tucker decomposition for the distribution data storage. Firstly, the tensor normalization processing model of structured SCADA data and unstructured video and picture data was established for heterogeneous data of distribution systems. Then, the paper used the tensor Tucker decomposition technique to deal with the distribution big data. This method can reserve the spatial eigenstructure of data when the heterogeneous data of distribution systems is compressed. In the end, using the real distributed system data to demonstrate that the proposed compression method can effectively reduce the amount of distributed system data. Compared with the singular value decomposition method, the results show that the proposed method is better than that of the singular value decomposition. © 2019 Chin. Soc. for Elec. Eng.
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收藏
页码：4744 / 4752
页数：8
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