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Transformer fault detection method based on SOM (Self Organizing Map) neural network

A transformer fault, neural network technology, applied in transformer testing, biological neural network models, instruments, etc., can solve the problem of only offline detection, and achieve the effect of simple algorithm, reduced computational load, and high self-organization

Active Publication Date: 2017-02-22
GUANGAN POWER SUPPLY COMPANY STATE GRID SICHUANELECTRIC POWER +1
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Problems solved by technology

[0006] The invention provides a transformer fault detection method based on SOM neural network, which solves the technical problem that the existing method for detecting the running state of the transformer can only perform offline detection, and realizes the technical effect of being able to perform online detection on the transformer

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  • Transformer fault detection method based on SOM (Self Organizing Map) neural network

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Embodiment Construction

[0048]The invention provides a transformer fault detection method based on the SOM neural network, which solves the technical problem that existing methods for detecting the running state of the transformer can only perform offline detection, and realizes the technical effect of being able to perform online detection on the transformer.

[0049] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, under the condition of not conflicting with each other, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0050] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways diff...

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Abstract

The invention discloses a transformer fault detection method based on an SOM (Self Organizing Map) neural network. The method comprises the following steps: S100: selecting a transformer as a testing object, and acquiring vibration signals of the transformer in different states as sample data; S200: decomposing and extracting a characteristic vector by utilizing ensemble empirical mode decomposition in Hilbert-Huang transform; S300: inputting the characteristic vector into the SOM neural network; S400: calculating a distance between a weight of a mapping layer and an input vector; S500: adjusting weights of an efferent neuron and an adjacent neuron; S600: judging whether pre-set conditions are met or not, and finishing SOM neural network training to obtain a testing sample; and S700: inputting the testing sample, and outputting the transformer fault type corresponding to the testing sample according to the network, thereby realizing the technical effect of online detection of the transformer.

Description

technical field [0001] The invention relates to the field of electric equipment detection, in particular to a transformer fault detection method based on SOM neural network. Background technique [0002] Among the various equipment in the power system, the transformer is an expensive and important equipment, and its reliability is of great significance to ensure the safe operation of the power grid. The fault statistics of transformers over the years show that transformer windings and iron cores are the most faulty components, and transformer accidents caused by windings and iron cores account for 70% of the total transformer accidents. [0003] At present, there are more and more methods for detecting the operation status of transformers. The short-circuit impedance method judges the winding deformation by measuring the reactance of the winding off-line and observing the change of its impedance value, but this method has low sensitivity and poor reliability. The low-volta...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/02G06N3/02
CPCG01R31/62G06N3/02
Inventor 李敏陈果石同春沈大千秦少鹏向天堂邓权伦罗宇昆高翔陈大浩王亨桂陈飞洋
Owner GUANGAN POWER SUPPLY COMPANY STATE GRID SICHUANELECTRIC POWER
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