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Time series data cleaning method based on correlation analysis and principal component analysis

A principal component analysis, time series technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as efficiency limitations, affecting accuracy, and being less sensitive to faults

Active Publication Date: 2016-05-04
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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Problems solved by technology

The three-ratio method is more suitable for some common serious faults, but it is not very sensitive to some low-frequency faults, because some codes do not correspond to the correct fault types
They can all be used to improve accuracy when dealing with fault data similar to electrical equipment, but because they only focus on the training process of machine learning and lack of preprocessing of collected data, there are obvious limitations in efficiency: that is, when the data size is getting larger and larger When , there is a lot of redundancy and noise in the training data, the training efficiency is low and the final accuracy is affected to a certain extent

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  • Time series data cleaning method based on correlation analysis and principal component analysis
  • Time series data cleaning method based on correlation analysis and principal component analysis
  • Time series data cleaning method based on correlation analysis and principal component analysis

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0046] Such as figure 1 As shown, a time series data cleaning method based on correlation analysis and principal component analysis includes the following steps:

[0047] Step S1: Use the Pearson coefficient analysis method (PCC) to find out other power data that has a hidden relationship with the transformer fault, and add new basis for fault diagnosis.

[0048] The present invention analyzes other electrical data at this stage using Pearson analysis (PCC). Pearson analysis method (PCC) is a measurement method widely used in pattern recognition, statistical analysis and image processing. The Pearson coefficient is a parameter indicating the degree of linear correlation between two data sets, and its value range is [-1, 1], 1 represents a complete positive correlation, -1 represents a complete negative correlation, and 0 represents no relationship, as...

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Abstract

The invention discloses a time series data cleaning method based on correlation analysis and principal component analysis, comprising the following steps: finding out hidden correlation between transformer faults and other power data by using a Pearson's correlation coefficient (PCC); reducing the dimension and noise of all relevant time series through principal component analysis (PCA); and inputting part of cleaned data as a training set into a BP neural network (BPNN) for training and learning, and taking the remaining data as a test set to verify a model. Compared with the traditional technology, the accuracy of transformer fault diagnosis is significantly improved, the accuracy of classification is improved, and the operation time is shorter for high-dimensional data.

Description

technical field [0001] The invention relates to a time series data cleaning method based on correlation analysis and principal component analysis. Background technique [0002] As the lifeblood of the national economy and security, the safe operation of the power grid has always been the core and essential requirement of the power grid. Oil-immersed transformers are crucial equipment in the power system. Their working status directly affects the safety of the entire power system, and any failure may cause serious economic losses. Therefore, it is necessary to predict potential failures in order to take appropriate solutions to repair equipment in time. Dissolved gas analysis (DGA) is an effective method for fault diagnosis of power transformers. According to the experience of the power industry, the working state of the transformer is mainly related to the content of dissolved gases in several oils, mainly including: hydrogen (H 2 ), methane (CH 4 ), acetylene (C 2 h 2...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/2135
Inventor 牛进苍陈玉峰张锦逵祝永新盛戈皞杜修明杨祎郭志红辜超朱文兵郑建李秀卫朱孟兆周加斌李程启马艳马强李欣阳刘鑫意刘梦云
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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