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Transformer fault diagnosis method based on back propagation (BP) neural network

A BP neural network, transformer fault technology, applied in the field of transformer fault diagnosis based on BP neural network, can solve problems such as easy diagnosis errors, inaccurate transformer faults, inability to make fault judgments, etc., to ensure safe and reliable operation and improve accuracy. rate effect

Inactive Publication Date: 2013-07-24
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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Problems solved by technology

However, the three-ratio method also has great limitations. Only when the content of each component of the dissolved gas in the oil exceeds the threshold, can the three-ratio method be used for transformer fault diagnosis
In addition, the lack of many codes in the three-ratio method will result in that the corresponding ratio combination cannot be found in the three-ratio coding rule table, and fault judgment cannot be performed; at the same time, if the calculated data is at the boundary of the three-ratio coding The transformer fault judged by the ratio method is not accurate, and it is easy to make a wrong diagnosis

Method used

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  • Transformer fault diagnosis method based on back propagation (BP) neural network
  • Transformer fault diagnosis method based on back propagation (BP) neural network
  • Transformer fault diagnosis method based on back propagation (BP) neural network

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Embodiment Construction

[0033] Such as figure 1 Shown, the transformer fault diagnosis method based on BP neural network of the present invention comprises the following steps:

[0034] A: Collect training sample data as an input vector;

[0035] B: Coding the fault type, compiling the correspondence table between the training sample and the fault type;

[0036] C: Construct the BP neural network and train the BP neural network until a satisfactory accuracy is achieved;

[0037] D: Diagnose the sample to be tested, input the sample to be tested into the BP neural network, output the vector after network calculation, and obtain the diagnosis result.

[0038] In the present invention, the training sample data are H2, CH4, C2H4, C2H2, C2H6 and CO gas content respectively. Install sensors in the transformer to observe the contents of various gases dissolved in the transformer oil, and the input value of the above-mentioned BP neural network can be obtained.

[0039] Establish a fault type matrix T (a...

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Abstract

The invention discloses a transformer fault diagnosis method based on a back propagation (BP) neural network. The transformer fault diagnosis method based on the BP neural network is capable of enabling content of characteristic gases in insulation oil to be used as input of the BP neural network, carrying out operation fault diagnosis of a transformer accurately, greatly improving accuracy of the fault diagnosis of the transformer, and ensuring that the transformer can run safely and reliably.

Description

technical field [0001] The invention relates to a transformer fault diagnosis method, in particular to a transformer fault diagnosis method based on BP neural network. Background technique [0002] The power transformer is one of the very expensive operating equipment in high-voltage electrical equipment, and it is also an important part of the power system. In recent years, with the rapid development of my country's economy and the continuous improvement of power technology, the power industry has gradually developed towards high voltage and large capacity, and the failure rate of power transformers has also shown an upward trend. Since transformer faults are usually accompanied by arcing, discharge, and violent combustion, once a transformer fails, it will require power outages for repairs, which will directly affect people's lives, and may further lead to accidents such as penetration breakdown and explosion of power equipment. , Seriously affect the safety, stability, r...

Claims

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Application Information

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IPC IPC(8): G06N3/02
Inventor 禹建丽
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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