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The invention discloses a CNN-based transformer fault diagnosis method

A transformer fault and diagnosis method technology, applied in the direction of instruments, measuring electrical variables, image data processing, etc., can solve the problems of inability to achieve real-time performance, waste of human resources, etc., to reduce computational complexity, improve efficiency, and strengthen Lu sticky effect

Pending Publication Date: 2019-05-31
EAST INNER MONGOLIA ELECTRIC POWER COMPANY +1
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Problems solved by technology

However, at present, manual analysis is still mainly used to deal with various problems of major equipment. This method will cause a lot of waste of human resources, and in many cases, it cannot achieve real-time performance.

Method used

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  • The invention discloses a CNN-based transformer fault diagnosis method
  • The invention discloses a CNN-based transformer fault diagnosis method
  • The invention discloses a CNN-based transformer fault diagnosis method

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Embodiment Construction

[0030] Hereinafter, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.

[0031] Please refer to Figure 1 to 3 , The embodiment of the present invention provides a CNN-based transformer infrared image fault diagnosis method, the method includes

[0032] Step 1. Obtain the monitoring image of the transformer;

[0033] Step 2: Input the monitoring image into a CNN judgment model, and obtain the probability value that the monitoring image is a transformer in a normal state;

[0034] Step 3. Determine whether the probability value is lower than the set probability threshold, and if so, determine that the transformer is in a fault state.

[0035] Prior to this, the monitoring images need to be preprocessed. In addition, in order to establish a model that can automatically diagnose the infrared monitoring images of the transformer, the method needs to build a complete and unified image library at the initial stage, ...

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Abstract

The invention discloses a CNN-based transformer fault diagnosis method. The method comprises the following steps of 1, obtaining a monitoring image of a transformer; 2, inputting the monitoring imageinto a CNN judgment model, and obtaining a probability value of the monitoring image being a transformer in a normal state; And step 3, judging whether the probability value is lower than a set probability threshold value or not, and if yes, judging that the transformer is in a fault state. According to the invention, the deep neural network is used to determine and diagnose the fault state of themonitoring image of the transformer; According to the method, the calculation complexity of traditional fault diagnosis is reduced, the efficiency of transformer fault identification and diagnosis inthe transformer substation and the accuracy of a diagnosis result can be effectively improved, the robustness is high, and monitoring images of different transformer substation backgrounds can be processed.

Description

Technical field [0001] The invention relates to the field of diagnosis and identification of key equipment in substations, in particular to a CNN-based transformer fault diagnosis method. Background technique [0002] In order to realize the intelligent management of the main power equipment of the substation and ensure the safe and reliable operation of the power system, the power company has strengthened the monitoring methods and means for the operation of the power equipment. But at present, the main method of manual analysis is still used to deal with the problems of the main equipment. This method will lead to a large amount of waste of human resources, and in many cases it cannot be real-time. [0003] Convolutional Neural Network (CNN) is one of the most representative network structures in deep learning technology. Summary of the invention [0004] In order to solve the above-mentioned problems in the background art, the present invention provides a CNN-based transformer f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06K9/62G01R31/00G06N3/04
Inventor 李文震张成松李昉张海龙吴启瑞高春辉彭仲晗谷凯凯陈凯李穆张辉沈厚明曹磊
Owner EAST INNER MONGOLIA ELECTRIC POWER COMPANY
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