Transformer fault diagnosis method based on improved semi-supervised classification of graph

A transformer fault diagnosis method technology, applied in the direction of instruments, character and pattern recognition, calculation, etc., to improve the accuracy and maintain the effect of safety

Inactive Publication Date: 2017-04-26
ELECTRIC POWER SCI RES INST OF GUIZHOU POWER GRID CO LTD
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  • Abstract
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  • Claims
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Problems solved by technology

[0005] Aiming at the problems existing in current transformer fault diagnosis, the present invention provides a transformer fault diagnosis method based on improved graph semi-supervised classification

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  • Transformer fault diagnosis method based on improved semi-supervised classification of graph
  • Transformer fault diagnosis method based on improved semi-supervised classification of graph
  • Transformer fault diagnosis method based on improved semi-supervised classification of graph

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Embodiment Construction

[0036] The implementation process of the method of the present invention will be described in detail below in conjunction with the accompanying drawings and actual cases. It should be emphasized that the following description is only exemplary and not intended to limit the scope of use of the present invention.

[0037] One, at first introduce following concrete method steps of the present invention, comprise:

[0038] Step 1: Obtain the content data of characteristic gases under various operating conditions of the transformer as a sample set for fault diagnosis, and perform normalization, among which, select H 2 、CH 4 、C 2 h 6 、C 2 h 4 、C 2 h 2As the characteristic gases for fault diagnosis, the contents of these five gases in transformer oil under various operating conditions are collected in real time to form a fault diagnosis sample set. The normalization method described is: for H 2 The content of the gas, its normalized value is H 2 The percentage value of the ...

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Abstract

The invention relates to a transformer fault diagnosis method based on improved semi-supervised classification of a graph. The method comprises the following steps that content data of characteristic gases of a transformer in various running states is acquired as a fault diagnosis sample set and normalized; a labeled sample and an unlabeled sample are selected from each class of samples for similarity learning, a similarity neighbor graph is constructed, and a weight matrix is calculated; an initial label matrix is constructed; the initial label matrix is standardized; semi-supervised classification modeling is conducted according to a fuzzy neighbor label propagation algorithm to generate a training model; class labels are allocated to the label-free test samples through the training model, and a diagnosis result is obtained. The method is applied to fault diagnosis of the transformer with the unbalanced sample set, and the high fault diagnosis precision can be achieved.

Description

technical field [0001] The invention belongs to the technical field of transformer fault on-line monitoring, and in particular relates to a transformer fault diagnosis method based on improved graph semi-supervised classification. Background technique [0002] The power transformer undertakes the task of transforming, distributing and transmitting electric energy in the power system. It is one of the important equipment of the power system, and its operating status directly affects the safety, stability and reliability of the entire power system. Although the safety level of power system operation is constantly improving, and the mechanical performance and electrical strength of power transformer design are also continuously improving, but due to the long-term operation of transformers in harsh environments such as heat, electricity and external damage, insulation aging and The material deteriorates, causing a malfunction. Once the electrical appliance breaks down, it will ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/28G06K9/62
CPCG01N33/28G06F18/24147
Inventor 刘君赵立进黄良曾华荣张迅彭辉陈欢龙嘉文王家华张凯
Owner ELECTRIC POWER SCI RES INST OF GUIZHOU POWER GRID CO LTD
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