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Transformer maintenance decision method based on hybrid model

A hybrid model and decision-making technology, applied in biological neural network models, instruments, special data processing applications, etc., can solve problems such as irreversible damage to transformers, adding new hidden dangers to transformers, and insufficient equipment maintenance.

Active Publication Date: 2014-05-21
STATE GRID CORP OF CHINA +2
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] ① The maintenance work is "one size fits all", which makes the equipment that should be repaired insufficient, and the blind repair that should not be repaired will not only cause a lot of waste of manpower, material and financial resources, but also increase the frequent disassembly of equipment during the excessive maintenance process. probability of hidden danger
②The withstand voltage test after maintenance will also cause irreversible damage to the transformer and reduce its overall life.

Method used

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  • Transformer maintenance decision method based on hybrid model
  • Transformer maintenance decision method based on hybrid model
  • Transformer maintenance decision method based on hybrid model

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Embodiment

[0083] Through the collection of monitoring data and historical life (time interval from putting into operation to the first failure) data of a SFSZ10-M-31500 / 110 oil-immersed transformer of Hebei Electric Power Company, these data are normalized After the RBF neural network training and MIV method feature variable screening, the feature variable with MIV value greater than 0.5 is used as the input variable of the PHM proportional failure model to construct the PHM failure model and verify the validity of the model.

[0084] 1 Data collection and feature variable screening

[0085] Part of the monitoring data and historical life (time interval from putting into operation to the first failure) data of a SFSZ10-M-31500 / 110 oil-immersed transformer collected from Hebei Electric Power Company is shown in Table 1. The total monitoring volume is 6, that is, degree of polymerization, furfural content, CO 2 / CO ratio, water content, partial discharge and top oil temperature.

[0086...

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Abstract

The invention relates to the technical field of transformer fault on-line monitoring, in particular to a transformer maintenance decision method based on a hybrid model. According to the method, a hybrid modeling mode of an RBF neural network and a PHM proportion failure model is adopted, so the advantage that the proportion failure model can be maintained according to needs is fully developed compared with a traditional maintenance mode, the screening function of the RBF neural network method and an MIV method on characteristic quantities is fully utilized, and accordingly the accuracy and reliability of maintenance decision schemes of the proportion failure model(PHM) are further improved.

Description

technical field [0001] The invention relates to the technical field of transformer fault online monitoring, in particular to a hybrid model-based transformer maintenance decision-making method. Background technique [0002] The power transformer is the core equipment of the power system, and its operating status is related to thousands of households. The best economic benefits of the transformer are increasingly dependent on its own aging condition monitoring, life evaluation and life extension technology. [0003] For a long time, the power industry has been using the maintenance system of insulation preventive test and regular maintenance. This maintenance mode has effectively reduced equipment accidents in practice for many years. However, with the rapid increase in the number of power grid equipment, its shortcomings are also increasing. Protruding, mainly manifested in: [0004] ① The maintenance work is "one size fits all", which makes the equipment that should be rep...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/02
Inventor 刘宏亮王永强岳国良潘瑾梁斌
Owner STATE GRID CORP OF CHINA
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