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Ubiquitous power Internet of Things perception data missing restoration method based on matrix filling

A power Internet of Things and sensing data technology, which is applied in the field of ubiquitous power Internet of Things sensing data missing repair based on matrix filling, and can solve problems such as consuming a lot of computing resources.

Active Publication Date: 2020-01-17
TIANJIN UNIV +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such methods consume more computing resources during execution and require a large amount of historical data for model training

Method used

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  • Ubiquitous power Internet of Things perception data missing restoration method based on matrix filling
  • Ubiquitous power Internet of Things perception data missing restoration method based on matrix filling
  • Ubiquitous power Internet of Things perception data missing restoration method based on matrix filling

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Experimental program
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Effect test

Embodiment 1

[0034] In order to overcome the above problems, an embodiment of the present invention proposes a method for complementing missing power quality data based on low-rank matrix theory. The low-rank matrix theory restores missing data based on the low-rank nature of the data itself, thus avoiding the data pre-training process.

[0035] By analyzing the inherent characteristics of typical power quality data, it is proved that it has data low-rank recoverability; on this basis, considering the mixed noise and peak abnormal values ​​of actual measurement data, a multi-norm joint low-rank optimization model is designed, and based on alternating The direction multiplier method is used to solve the model; at the same time, to solve the problem of slow model iteration, an adaptive iterative step selection method is proposed; finally, the effect of the algorithm is verified by analyzing the data recovery effect in high-frequency fault scenarios.

[0036] First, the measured one-dimension...

Embodiment 2

[0041] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0042] 1) Original sampling data reconstruction

[0043] The original sampling data are all one-dimensional time-domain sequences, which cannot be directly recovered, so they need to be organized into a matrix form.

[0044] Without loss of generality, it is assumed that a measuring point in the power grid collects data of n consecutive cycles, and each cycle f c subsampling. For the original data l, the slice transformation method is used to reconstruct it from a one-dimensional sequence in the time domain into a matrix form. The specific way is to follow the quarter cycle as the unit figure 1 The method organizes the data into a measurement data matrix L. Obviously, the size of the matrix L is related to the sampling time and sampling rate, which is 0.25f c ×4n order; at the same time, the transformati...

specific example

[0153] This section tests the effect of the recovery algorithm through the Real-life Power Quality Transients public data set. Select high-frequency fault scenarios: data under voltage swell, voltage sag, voltage interruption, pulse oscillation, and harmonic pollution. 50 cycles of data are extracted for each type of fault, and the data sampling rate is 20kHz, so there are 20,000 data points in total. At the same time, considering that random missing data will lead to fluctuations in the recovery effect, all results are obtained by taking the average value after repeating the experiment 30 times. Respectively verify the restoration effect of the singular value threshold algorithm SVT, the alternating direction multiplier method ADMM and the improved alternating direction multiplier method IADMM algorithm proposed by the present invention; on this basis, intercept a section with a 50% data missing rate, and observe the missing data recovery accuracy statistical distribution. ...

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Abstract

The invention discloses a ubiquitous power Internet of Things perception data missing restoration method based on matrix filling. The ubiquitous power Internet of Things perception data missing restoration method comprises the following steps: reconstructing measured one-dimensional time series data into a matrix form through slice transformation; obtaining a low-rank intensity index used for verifying data recovery feasibility; considering the structural characteristics of different components of the measurement data, establishing an optimization model for recovering missing data based on a low-rank matrix filling theory, constraining various noises through a matrix norm, and eliminating the noise influence; and obtaining an iterative calculation formula for quickly solving the model by improving an alternating direction multiplier method, and realizing recovery of missing measurement data. According to the ubiquitous power Internet of Things perception data missing restoration method, under the condition that part of the measurement data is lost and various forms of noise such as Gaussian noise and peak abnormal value are mixed, the original complete measurement data is recoveredbased on the low-rank matrix filling theory, and then complementation of the missing data is achieved.

Description

technical field [0001] The present invention relates to the field of electric power Internet of Things, in particular to a matrix filling-based ubiquitous electric power Internet of Things perceived data loss repair method. Background technique [0002] The construction of ubiquitous power Internet of Things has become a key goal of power grid transformation and upgrading. In its architecture, the sensing layer responsible for sensing and collecting data is at the bottom layer, and the acquired measurement data is the basis for supporting the entire system. However, like any industrial site measurement data, data loss may occur during the process of data perception, transmission, and processing. At this time, if the missing data can be recovered based on the inherent structural characteristics of the data, the integrity of the data can be guaranteed and the value of the data can be improved. [0003] The traditional completion of missing measurement data is often based on ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06
CPCG06Q10/04G06Q10/06393G06Q50/06Y04S10/50
Inventor 杨挺李扬张璐何周泽
Owner TIANJIN UNIV
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