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

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 ...

AI Technical Summary

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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