Method for diagnosing fault of oil-immersed transformer on basis of rough set and bayesian network

Abstract

The invention discloses a method for diagnosing a fault of an oil-immersed transformer on the basis of a rough set and a bayesian network. The method comprises the following steps that (a) the type of the fault is determined, as much as possible input fault characteristic vectors are selected in an original sample set, and an input attribute set is determined; (b) discretization processing is carried out on a fault data set through a data discretization method in the rough set theory, and a discretization decision table is established; (c) establishment of the bayesian network is carried out through Matlab; (d) a conditional probability table is initialized, wherein all the possible conditional probabilities of each node relative to the father node of the node and the quantitative description of the corresponding problem domain are listed in the conditional probability table; (e) parameter learning is carried out, and a deduction engine is established to carry out deduction after the bayesian network is established; (f) a test sample set is input, the posterior probability is solved, and the type of the fault is judged. The method for the oil-immersed transformer on the basis of the rough set and the bayesian network can simplify the scale of a diagnosis network, enhance the anti-interference performance of the network, diagnose various faults of the transformer rapidly, and reduce the outage rate of the transformer greatly.

Description

[0001] technical field [0002] The invention relates to a fault diagnosis method for an oil-immersed transformer, in particular to an online fault diagnosis method for an oil-immersed transformer based on the combination of rough sets and Bayesian networks. Background technique [0003] The power transformer is one of the most important equipment in the power system. Its normal operation is the basic guarantee for the reliable power supply of the entire power system. However, due to the complexity and diversity of power transformer faults, and the causes of these faults are very complicated and not obvious , making it very difficult to accurately judge the fault type of power transformers, so the research on its fault diagnosis technology has always been the focus of domestic and foreign scholars. [0004] The three-ratio method based on the analysis of dissolved gas in oil is the most widely used diagnostic method at present. It is simple and easy to use, but it also has m...

AI Technical Summary

Problems solved by technology

This method classifies faults based on transformer monitoring data according to the maximum a posteriori probability criterion, which is helpful for accurate and rapid diagnosis of transformer fault types, but does not consider the relationship between various attribute variables, the content of various gases in oil or various The relationship between the gas ratio and various transformer faults is closely connected and very complicated, and the weight of each attribute variable is different. This method only takes C2H 2, C2H4, CH4, H2, C2 H6, CO2, and CO, these characteristic gases are respectively used as attribute variables, which can easily cause ambiguity in diagnosis results, and failures cannot even be identified

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