A Transformer Fault Diagnosis Method Based on Bayesian Network

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 ...

AI Technical Summary

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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