基于分布式压缩感知和边缘计算的配电网电能质量数据压缩存储方法

doi:10.19595/j.cnki.1000-6753.tces.200422

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Abstract In view of the increasingly large power quality data in the distribution network and the difficulty in dividing harmonic pollution, this paper proposes a power quality data compression storage method based on distributed compressed sensing and edge computing. Its innovation lies in the proposed adaptive joint reconstruction algorithm based on the synchronous orthogonal matching pursuit algorithm and the K-SVD dictionary learning algorithm, which is applied to the cloud-side collaboration framework with distributed compressed sensing as the edge algorithm. Therefore, the dictionary atoms and measured values uploaded on the edge are analyzed to achieve compressed storage of power quality data. In addition, dynamic of harmonic pollution of the distribution network can be realized based on the cross-correlation between the sparse coefficients of each node. The simulation results show that the algorithm can not only compress realize the compression of the power quality data with high accuracy, save data storage space, but also has reference significance for the harmonic pollution partition of the distribution network.

Key words： Distribution network distributed compressed sensing edge computing K-SVD algorithm harmonic pollution division

Received: 27 April 2020

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TM74

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	Wang He
	Li Shiqiang
	Yu Huanan
	Zhang Jian

Cite this article:

Wang He,Li Shiqiang,Yu Huanan等. Compression Acquisition Method for Power Quality Data of Distribution Network Based on Distributed Compressed Sensing and Edge Computing[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4553-4564.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200422 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I21/4553

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