Fault diagnosis and early warning method based on multi-source data fusion and convolutional twin neural network

A neural network and multi-source data technology, which is applied in the field of fault diagnosis and early warning based on multi-source data fusion and convolution twin neural network, can solve problems such as new hidden dangers, insufficient maintenance, and excessive maintenance, and improve the efficiency of fault diagnosis Effect

Pending Publication Date: 2022-03-01
STATE GRID LIAONING ELECTRIC POWER CO LTD SHENYANG POWER +2
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Problems solved by technology

[0002] For a long time, my country's maintenance strategy for transformers has mainly adopted time-based regular maintenance, which has the defects of "over-maintenance" and "insufficient maintenance", which is blind and mandatory, resulting in a lot of waste of manpower and material resources. And it increases the probability of new hidden dangers

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  • Fault diagnosis and early warning method based on multi-source data fusion and convolutional twin neural network
  • Fault diagnosis and early warning method based on multi-source data fusion and convolutional twin neural network
  • Fault diagnosis and early warning method based on multi-source data fusion and convolutional twin neural network

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[0074] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

[0075] In order to realize online fault monitoring of power transformers through artificial intelligence and deep learning, this implementation scheme provides a fault diagnosis and early warning method based on multi-source data fusion and convolution twin neural network, see figure 1 , the diagnostic method includes the following steps:

[0076] S1: Collect the vibration information of the po...

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Abstract

The invention discloses a fault diagnosis and early warning method based on multi-source data fusion and a convolutional twin neural network, and the method comprises the steps: constructing a data dictionary for various types of data in the collected vibration information of a power transformer, the concentration information of gas dissolved in oil, the infrared information of the power transformer, the voltage information and the current information; standardized integration is carried out on the collected data, data information is extracted through double-space features, fault identification and classification of the power transformer are realized by using the convolutional twin neural network, and the fault diagnosis efficiency can be improved.

Description

technical field [0001] The present invention relates to the technical field of power transformer fault diagnosis, in particular to a fault diagnosis and early warning method based on multi-source data fusion and convolution twin neural network. Background technique [0002] For a long time, my country's maintenance strategy for transformers has mainly adopted time-based regular maintenance, which has the defects of "over-maintenance" and "insufficient maintenance", which is blind and mandatory, resulting in a lot of waste of manpower and material resources. And it increases the probability of new hidden dangers. Considering the big data characteristics of transformer operation information and the diversity and complexity of fault types, the new round of technological revolution and industrial transformation requires power equipment to transform to intelligent and high-end. The artificial intelligence algorithm can establish a simulated nervous system for information processi...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/00G06Q50/06
CPCG06N3/08G06Q10/20G06Q50/06G06N3/045G06F18/28G06F18/241G06F18/253G06F18/214
Inventor 王振浩许超李小兰杨波李泽曦张琦刘东延卢毅杨旭郑舒文谭澈赵宁孙守道谢杰赵贝加张志鹏
Owner STATE GRID LIAONING ELECTRIC POWER CO LTD SHENYANG POWER
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