Transformer fault diagnosis method based on Bayesian network

A transformer fault and Bayesian network technology, applied in the direction of instruments, measuring electrical variables, measuring devices, etc., can solve problems such as transformer faults that do not consider the connection and inapplicable fault factors, so as to improve maintenance efficiency, reduce operating costs, and quickly The effect of accurate diagnosis

Active Publication Date: 2013-08-14
YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST +1
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Problems solved by technology

This method can effectively solve the problems of missed judgment and misjudgment in the existing judgment methods, but it does not consider the relationship between various factors, and is not suitable for the diagnosis of transformer faults with complex working environments and uncertain fault factors.

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  • Transformer fault diagnosis method based on Bayesian network
  • Transformer fault diagnosis method based on Bayesian network
  • Transformer fault diagnosis method based on Bayesian network

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Embodiment

[0029] The invention is based on the real-time operation data of transformers accumulated by Yunnan power grid, and further explains the invention and the effectiveness of the invention in combination with the accompanying drawings. Refer to attached figure 1 The process of establishing Bayesian network model based on fault data is shown, and the steps of establishing Bayesian model for transformer fault diagnosis are described in detail.

[0030] Step 1: Determine the transformer faults that can be diagnosed by dissolved gas in transformer oil. According to the fact that the common faults of Yunnan Power Grid Company, determine the fault set as medium temperature overheating (300-700) ℃, low temperature overheating (less than 150 ℃), low temperature overheating (150 ~300) ℃, low-energy discharge and overheating, the attached table 1 shows the transformer fault data. The hydrogen, methane, acetylene, ethylene, ethane, total hydrocarbons, carbon monoxide, carbon dioxide and ot...

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Abstract

The invention relates to a transformer fault diagnosis method based on a Bayesian network. According to the method, gas dissolved in oil of a transformer is analyzed by adopting a three-ratio method; data about gas is obtained in a real operation environment; study of structures and parameters of the Bayesian network is accomplished by adopting a TAN (Tree Augmented Naive) algorithm; a fault diagnostic model is established, and an expert system is utilized for correcting the fault diagnostic model; and the fault diagnostic model is used for diagnosing real-time operation states of the transformer. The method has the benefits that the problem about fault diagnosis for the transformer under the condition of uncertainty and lacking given information is solved, and meanwhile, an importance analytical method based on the Bayesian network is introduced to play a certain assistant role in analysis of the fault mechanism. The method can quickly and accurately diagnose the fault of the transformer, provide support for establishment of a maintenance decision for the transformer, effectively improve the maintenance efficiency, and lower the operation cost of a power system.

Description

technical field [0001] The invention belongs to the field of equipment maintenance and guarantee, and specifically uses a method for diagnosing the real-time running state of the equipment based on the equipment Bayesian network model and detection information. Background technique [0002] The transformer is an important operating equipment in the power system, and its operating status is directly related to the safety and reliability of the power system. The internal structure of the transformer is complex, and the operating environment is special. Under the working state, it must bear the environmental stress caused by complex heat, electricity, machinery and humidity. The main result of the interaction of these stresses is the deterioration and aging of the transformer insulation. In oil-immersed transformers, insulation degradation and aging are the main causes of early failure of equipment. Among the existing routine transformer fault diagnosis methods, dissolved gas ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/00
Inventor 张文斌王达达张少泉陈晓云孙树栋蔡志强徐先新
Owner YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST
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