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Transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost

A technology of transformer fault diagnosis method, which is applied in transformer testing, instrumentation, measuring electrical variables, etc., and can solve problems such as low accuracy of transformer fault diagnosis

Inactive Publication Date: 2018-10-30
XI'AN POLYTECHNIC UNIVERSITY
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Problems solved by technology

[0003] The purpose of the present invention is to provide a transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost, which solves the problem of low accuracy of transformer fault diagnosis in the prior art

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  • Transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost
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  • Transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost

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[0067] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0068] A transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost of the present invention, the flow chart is as follows image 3 As shown, the specific steps are as follows:

[0069] Step 1. First, the sample set S={(x 1 ,t 1 ),(x 2 ,t 2 ),…,(x i ,t i )} for classification, i is a positive integer, and each category is divided into training samples L and test samples U according to a ratio of 3:1, where x i =x i1 ,x i2 ,x i3 ,x i4 ,x i5 Represents sample attributes, x i1 ~x i5 Corresponding to the five properties of hydrogen, methane, ethane, ethylene, and acetylene respectively, Represents category labels 1, 2, 3, 4, and 5, corresponding to normal state, medium and low temperature overheating, high temperature overheating, low-energy discharge, and high-energy discharge;

[0070] Step 2. Normalize t...

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Abstract

The invention discloses a transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost. The method comprises the following steps: a M-RVM classification model is established based on the transformer characteristic data training, then each test sample is tested and the information entropy of each sample is calculated, the training samples are screened by information entropy, and the selected samples are used to train the base classifier A-MRVM based on AdaBoost, finally, the samples to be tested are classified and classified by M-RVM classifier and the information entropyis calculated, and the information entropy is compared with the information entropy threshold, if the information entropy is smaller than the threshold, the classification result of M- RVM classifieris as the output, whereas multiple A-MRVM base classifier are used to continues to classify the samples, based on the classification of the tested samples of each A-MRVM base classifier, the weightedcoefficient of the base classifier and the final strong classifier is weighted and integrated to improve the diagnostic accuracy of the whole algorithm.

Description

technical field [0001] The invention belongs to the technical field of transformer fault online monitoring, and in particular relates to a transformer fault diagnosis method based on M-RVM fusion dynamic weighted AdaBoost. Background technique [0002] With the development of smart grid and the continuous increase in demand for electricity consumption, the scale of the power system and the capacity of transformers continue to expand, resulting in increasing losses to the national economy caused by transformer failures. Therefore, accurate diagnosis of the operating status of transformers is crucial. The safe operation of the power system is of great significance. Dissolved gas analysis (DGA) is mostly used to diagnose internal faults of power transformers. However, these methods, such as IEC three-ratio method and Rogers ratio method, have problems such as too absolute ratio boundaries and incomplete coding, which may cause misdiagnosis of faults. In recent decades, with th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/02
CPCG01R31/62
Inventor 黄新波王享朱永灿曹雯
Owner XI'AN POLYTECHNIC UNIVERSITY
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