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Power Distribution System Stream Data Compression Based on Incremental Tensor Decomposition
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DOI：10.1109/TII.2019.2934766.png)
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To overcome the problem of storing massive stream data in power distribution systems, this article presents a method of stream data compression for power distribution systems, which is based on tensor decomposition. First, to preserve the spatial structure of high-dimensional data in the presentation stage, we establish a tensor representation model of multi phasor measurement unit stream data and video stream data of power distribution systems. Then, a stream data compression method that is based on incremental tensor decomposition is proposed. Compared with the traditional method, the proposed method uses the characteristics of a large amount of data accumulated over time in the distribution system. We use a small amount of real-time newly added data to update the compression results of existing historical data, thereby avoiding the direct compression of accumulated data and reducing the time and space complexities of the traditional method. Therefore, the proposed approach can compress the stream data in less time and with lower memory consumption. Finally, the effectiveness of the method is verified using real data.
Keyword:
Matrix decomposition
Power distribution
Phasor measurement units
Data compression
Real-time systems
Streaming media
Incremental tensor decomposition
power distribution systems
stream data compression
tensor representation model
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