Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value

A K-means and fault detection technology, which is applied to vibration measurement in solids, instruments, and measurement devices, can solve the problem that the SVM algorithm cannot be practically applied in the field of transformer fault diagnosis, and achieve the effect of improving detection performance and performance

Active Publication Date: 2014-12-31
STATE GRID CORP OF CHINA +2
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Problems solved by technology

[0006] The purpose of the present invention is to solve the defects existing when wavelet analysis is applied to transformer fault detection for feature extraction and the deficiency that SVM algorithm cannot be practically applied in the field of transformer fault diagnosis, and provides a method based on wavelet GGD features and different Transformer Online Fault Detection Method Based on Balanced K-means Downsampling and SVM

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  • Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value
  • Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value
  • Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value

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specific Embodiment approach 1

[0037] Specific implementation mode one: the following combination Figure 1 to Figure 4 Describe this embodiment, the transformer online fault detection method based on wavelet GGD feature and unbalanced K-means downsampling integrated SVM described in this embodiment, the method includes the following steps:

[0038] Step 1, using the vibration acceleration sensor installed on the transformer chassis to collect the transformer vibration signal;

[0039] Step 2. Perform low-pass filtering on the transformer vibration signal obtained in step 1 to remove high-frequency noise information and obtain a noise-reduced vibration signal.

[0040] Step 3: Segment the noise reduction vibration signal obtained in step 2 according to the time series, and use the db20 wavelet of the Daubechies wavelet series to perform five-layer static wavelet analysis on the segmented time series, and extract the GGD parameters of the wavelet transform of each layer , the five-layer GGD parameter combin...

specific Embodiment approach 2

[0075] Specific implementation mode two: the following combination Figure 1 to Figure 22 This embodiment will be described, and a specific example will be given in this embodiment.

[0076]First collect fault samples: In order to reflect the vibration characteristics of the box and avoid the attenuation of the vibration of the winding and the iron core to the greatest extent, it is necessary to select multiple vibration sensors. In this embodiment, 6 vibration acceleration sensors are selected and fixed on the side and upper and lower ends of the transformer oil tank respectively, among which 4 vibration acceleration sensors are arranged on the side of the oil tank, and 1 vibration acceleration sensor is arranged on the upper and lower ends of the oil tank, located in the middle of the upper and lower end faces . The vibration acceleration sensor is firmly adsorbed on the 1 / 2 of the corresponding side of the transformer winding through the permanent magnet, and the surface o...

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Abstract

The invention relates to a transformer on-line fault detecting method base on a sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feathers and unbalanced K-mean values, and belongs to the field of transformer fault detection. The method aims at overcoming the defects caused when the wavelet analysis is applied to the transformer fault detection for carrying out feather extraction in the prior art. The transformer on-line fault detecting method comprises the steps that 1, vibration signals of a transformer are collected; 2, low-pass filtering processing is carried out, high-frequency noise information is removed, and noise reduction vibration signals are obtained; 3, the noise reduction vibration signals are subjected to segment processing according to time series, db20 wavelets in Daubechies wavelet series are subjected to five-layer static wavelet analysis, each layer of wavelet conversion GGD parameters are extracted, five layers of GGD parameters are combined to be used as fault detection feather data, and the fault detection feather data is respectively used as training samples and testing samples; 4, the training samples are utilized for training a SVM detector; and 5, the testing samples are input into the trained SVM detector, and the on-line fault detection of the transformer is realized.

Description

technical field [0001] The invention relates to a transformer online fault detection method based on wavelet GGD features and unbalanced K-mean downsampling integrated SVM, belonging to the field of transformer fault detection. Background technique [0002] As the key equipment of the power system, the transformer can operate safely and reliably is the key to the safe operation of the entire power grid. To make the main transformer operate safely and improve the reliability of power supply, it is necessary to improve the operation, maintenance and repair level of the main transformer. Among them, effective fault monitoring for it plays a very important role in ensuring its safe operation and improving the reliability of the power system. Therefore, it is a work of practical significance to carry out transformer operation status monitoring. [0003] In the long-term theoretical research and engineering practice, some effective technical means and methods have been realized ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01H1/00
Inventor 刘福荣陶新民孙福军田伟张凯李震韩钰孙奇志张歆炜
Owner STATE GRID CORP OF CHINA
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