A method for repairing missing points of transformer operation data based on functional principal component analysis and wavelet transform is presented

A technology of functional principal components and operating data, which is applied to instruments, character and pattern recognition, computer components, etc., can solve the problems of low model applicability, difficult functional models, and time-consuming and labor-intensive manual filling.
CN109543769AInactive Publication Date: 2019-03-29ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
Publication Date
2019-03-29
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a method for repairing the missing points of transformer operation data based on functional principal component analysis and wavelet transform, which comprises the following steps: analyzing the collected operation data points by using FPCA method, and fitting the operation data function xi (t) on the whole time series; obtaining The residual function Epsilon (t) by the difference between the original data point and the data point obtained by FPCA. The residual function Epsilon (t) is denoised by wavelet transform, and Epsilon '(t) is obtained. The estimation function (shown in the description) substitutes time t0 at the missing point (shown in the description) to obtain (showin the description) as the repair value at the missing point. The invention adopts the modeof combining the FPCA and the wavelet transform, can grasp the characteristics of the data set as a whole, and can improve the local fitting degree. Compared with the traditional fitting method, theprediction value of the invention is more reliable.
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Description

technical field

[0001] The invention relates to the field of power equipment data cleaning, in particular to a method for repairing missing points in transformer operating data based on functional principal component analysis and wavelet transform. Background technique

[0002] Transformer is one of the most important equipment in the power grid, and its operation data is crucial for subsequent big data analysis. However, in the process of data collection and transportation, data may be missing due to some faults and human factors, which is not conducive to subsequent data analysis and data mining, so it is necessary to fill in the missing data.

[0003] At present, the commonly used missing data filling methods include: manual filling, interpolation, regression, etc. However, manual filling is time-consuming and laborious, and it is impractical to manually fill the huge amount of data generated every day; the reliability of the interpolation method is not high, and the int...

Claims

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