Transformer fault diagnosing method based on neural network

A transformer fault and neural network technology, applied in biological neural network models and other directions, can solve the problems of inability to determine the type of fault, indistinguishable by the three-ratio method, and inability to diagnose, to achieve reliability, safety and reliability, and cost reduction. Effect

Inactive Publication Date: 2013-08-28
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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Problems solved by technology

However, there are also some problems with the three-ratio method: when the characteristic gas content in the oil does not reach the attention value, this method cannot be used for diagnosis; , it is impossible to determine the fault type, and when multiple faults occur at the same time, the three-ratio method is difficult to distinguish

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  • Transformer fault diagnosing method based on neural network
  • Transformer fault diagnosing method based on neural network
  • Transformer fault diagnosing method based on neural network

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Embodiment Construction

[0015] like figure 1 Shown, a kind of method of transformer fault diagnosis based on BP neural network of the present invention comprises the following steps:

[0016] (1) Use the IEC three-ratio method to obtain the three-ratio values ​​of five gases: C 2 h 2 / C 2 h 4 、CH 4 / H 2 、C 2 h 4 / C 2 h 6 as training sample data;

[0017] (2) Use the membership function to fuzzify the three ratios;

[0018] (3) Encode the fault type according to the corresponding relationship between the fuzzy sample data and the fault type;

[0019] (4) Train the BP neural network according to the fuzzy training sample data until the BP network meets the accuracy requirements and enter the next step;

[0020] (5) Input the fuzzy sample data to be tested into the trained BP neural network;

[0021] (6). The BP neural network outputs the diagnosis result.

[0022] BP (Back Propagation) neural network, that is, the learning process of the error back propagation algorithm, consists of two p...

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Abstract

The invention discloses a transformer fault diagnosing method based on a neural network. The method comprises the following steps: obtaining three ratios of five kinds of gases: C2H2/C2H4, CH4/H2 and C2H4/C2H6 serving as training samples by using an IEC three-ratio method; (2) fuzzifying the three ratio by using a membership function; (3) encoding a fault type; (4) training a BP (Back Propagation) neural network according to fuzzified training sample data till the BP network meets the accuracy requirement; (5) inputting the fuzzified sample data to be detected into the trained BP neural network; and (6) outputting a diagnosis result by using the BP neural network. According to the method, the neural network is trained through history data, so that an online monitoring function can be realized by using the neural network. Data information on a transformer is acquired through a computer, and faults are diagnosed in advance, so that hidden danger is eliminated.

Description

technical field [0001] The invention relates to a transformer fault diagnosis method, in particular to a transformer fault diagnosis method based on a BP neural network. Background technique [0002] At present, as an important equipment of the power supply network, the transformer plays a particularly important role in the quality of the power supply network. Transformer faults will spread to a wide range, causing serious impacts and huge losses. Therefore, the fault repair and regular maintenance of transformers have always been the focus of attention. The regular maintenance system has been generally implemented in my country's electric power industry, which once played an effective supervisory role in ensuring the safe and stable operation of the power system. However, after many years of operation practice, it is found that due to the lack of pertinence in the maintenance plan, the regular maintenance system often easily leads to the double defect of over-maintenance ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/02
Inventor 禹建丽
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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