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The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing

A power data, compressed sensing technology, applied in electrical components, code conversion, etc., can solve the problems of unsatisfactory reconstruction results, not considering the correlation of monitoring data, and representing power data, so as to reduce transmission overhead and achieve good sparse representation. , the effect of reducing overhead

Active Publication Date: 2019-06-18
HUNAN UNIV
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Problems solved by technology

However, there are many deficiencies in the application of compressed sensing technology to smart grid data collection: ①Although previous studies have verified the effectiveness of compressed sensing for low-overhead data collection, most of these studies select a known matrix as the sparse The matrix is ​​reconstructed, and the selection of sparse matrix depends on the experience of the designer, resulting in unsatisfactory reconstruction results
Moreover, the existing known sparse matrix is ​​difficult to represent power data well
②The existing smart grid power data acquisition framework based on compressed sensing only uses the correlation of monitoring data at close times of a single intermediate station, and does not consider the correlation of monitoring data at close times between multiple intermediate stations

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  • The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing
  • The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing
  • The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing

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Embodiment Construction

[0033] The concrete realization process of the present invention is as follows:

[0034] The first step: the smart grid intermediate station collects the original power data f, and uses the measurement matrix Φ to obtain the compressed observation signal y=Φf;

[0035] Step 2: Upload the compressed observation signal y to the smart grid data center;

[0036] Step 3: Construct the power sparse matrix Ψ;

[0037] Step 4: Jointly restore multiple sets of observation data in the smart grid data center through the joint reconstruction algorithm to obtain the original power data;

[0038] Step 5: Smart grid power data collection is successful.

[0039] The present invention is further described below.

[0040] The smart grid system can be roughly divided into three parts: data center, intermediate station and user end (smart meter). One data center station (terminal station) corresponds to multiple intermediate stations (APs), and one intermediate station corresponds to multiple...

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Abstract

The invention discloses a low-overhead power data acquisition method based on distributed compressed sensing. The overhead for acquiring power data in an intelligent power grid is reduced. The electric power sparse matrix can perform better sparse representation on the electric power data. According to the joint reconstruction algorithm, the original data can be accurately reconstructed at the terminal, and the power data transmission overhead is reduced to a great extent.

Description

technical field [0001] The invention relates to a low overhead power data acquisition method based on distributed compressed sensing. Background technique [0002] In recent years, with the development of science and technology, the power grid is transforming into a smart grid, which is gradually combined with advanced sensors and smart infrastructure. How to accurately assess the state of the power system becomes the key to the power transmission network. State estimation requires sampling power data, including voltage, current, power, etc., through meters at different locations in the transmission network. The correct operation of the smart grid depends on the collection of fine-grained state detection data of the power grid. However, the direct transmission of the original detection data brings a large amount of transmission and storage overhead. [0003] In response to this problem, the Nyquist theorem can greatly reduce the data acquisition overhead. In this way, as ...

Claims

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Application Information

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IPC IPC(8): H03M7/30
Inventor 雷斐谢鲲肖玲田家政
Owner HUNAN UNIV
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