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Transformer fault diagnosis method based on radial basis function neural network

A technology based on neural networks and transformer faults, applied in the field of transformer fault diagnosis based on radial basis neural networks, can solve problems such as easy diagnosis errors, inaccurate transformer faults, and inability to make fault judgments, so as to ensure safe and reliable operation, The effect of improving accuracy

Active Publication Date: 2013-08-07
河南正数智能科技有限公司
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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 radial basis function neural network
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[0030] Such as figure 1 As shown, the transformer fault diagnosis method based on radial basis neural network of the present invention comprises the following steps:

[0031] A: Collect the training sample data as the input vector; the training sample data are H2, CH4, C2H4, C2H2, C2H6 and CO2 gas content, and the training sample data is first normalized and then input into the network. The normalization formula is x i =(x i -x min ) / (x max -x min ), where Xi represents the value of the characteristic gas, and X min Indicates the smallest numerical value among all gases, X max Indicates the numerical value with the largest value among all gases;

[0032] B: Coding the known training samples and corresponding fault types respectively, and compiling the corresponding table of training samples and fault types; the corresponding table of training samples and fault types is: if the code is 100000, the fault type is low temperature and overheating; if If the code is 010000, ...

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Abstract

The invention discloses a transformer fault diagnosis method based on a radial basis function neural network. According to the method, the content of characteristic gas in insulating oil can be used as input for the radial basis function neural network, transformer faults are output accurately, and accordingly accuracy in transformer fault diagnosis is improved greatly and safe and reliable transformer operation is ensured.

Description

technical field [0001] The invention relates to a transformer fault diagnosis method, in particular to a transformer fault diagnosis method based on a radial basis neural network. Background technique [0002] The power transformer is one of the most important equipment in the national power system, and it is also one of the equipment with the most faults in the power system. Its operating status directly affects the security of the national power system. Therefore, it is of great significance to study the transformer fault diagnosis technology and improve the reliability and safety of the transformer. [0003] In the study of transformer fault diagnosis, there is a complex nonlinear mathematical relationship between fault symptoms and fault types, which makes it difficult to find a suitable mathematical model for diagnosis. Among them, the internal faults of transformers are manifested in three types: mechanical, electrical and thermal, and the latter two are the most impo...

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Application Information

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IPC IPC(8): G06N3/02
Inventor 禹建丽
Owner 河南正数智能科技有限公司
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