Unbalanced data set-oriented extreme learning machine based transformer fault diagnosis method

Abstract

The invention discloses an unbalanced data set-oriented extreme learning machine based transformer fault diagnosis method. The method specifically comprises the following steps: step 1, dividing a collected unbalanced sample set S={(x1, t1), (x2, t2)...(xn, tn)} with class labels of an oil-immersed transformer into training samples and test samples by a ratio of 6:1, wherein xi represents a sampleproperty, i may be equal to 1, 2, 3, 4, 5, 6, and specifically comprises six attributes of hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide, ti represents a class label, i may be equalto 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 respectively corresponding to normal state, middle temperature overheat, high temperature overheat, partial discharge, spark discharge, arc discharge, and the tiis clustered by a PAM algorithm; step 2, for minority classes, taking the cluster center of the PAM algorithm as a central point; and step 3, during the classification output stage of the extreme learning machine, firstly establishing a DAG-ELM model, secondly dividing a new data set generated in step 2 into training sets and test sets by the ratio of 6:1, wherein 6 parts are used for training modeling, and 1 part is used for verifying the classification effect. According to the unbalanced data set-oriented extreme learning machine based transformer fault diagnosis method, the influence of the unbalanced data set on the transformer fault diagnosis result is solved.

Description

technical field [0001] The invention belongs to the technical field of transformer fault online monitoring, and in particular relates to an extreme learning machine transformer fault diagnosis method for unbalanced data sets. Background technique [0002] At present, the rapid development of society and the rigid demand for power grid construction brought along with it have basically formed the pattern of national networking. The power system is a large system connected by many electrical equipment for transmission, transmission, transmission and distribution. The failure of any equipment will directly affect the stability and safety of the entire system. Transformers are widely used in power systems, and their safe and stable operating status is related to the safety of the power grid and people, and failures will cause great economic losses. [0003] Therefore, it is very important to carry out fault diagnosis on transformers. Its main faults include high temperature ove...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a kind of extreme learning machine transformer fault diagnosis method oriented to unbalanced data sets, this method can solve the limited amount of information contained in a small number of faul

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