Fault diagnosis method for oil immersed reactor based on IFOA optimized SVM model

Abstract

The invention discloses a fault diagnosis method for an oil immersed reactor based on an IFOA optimized SVM model. The method comprises the following steps: preprocessing fault diagnosis data of the reactor, constructing training samples and test samples; establishing a fault diagnosis model of the reactor based on a support vector machine; improving a fruit fly algorithm; establishing a fault diagnosis model of the oil immersed reactor based on IFOA-SVM; and substituting sample data into the fault diagnosis model to achieve fault diagnosis of the reactor. The fault diagnosis method for the oil immersed reactor based on the IFOA optimized SVM model proposed by the invention is suitable for training of small data set of samples. The improved fruit fly algorithm realizes dynamic balance between the global search capability and the local optimization ability, and can effectively avoid the problem of local optimum. The improved fruit fly algorithm is applied to the selection of a penalty parameter C and a kernel function g of the support vector machine, which can realize the optimal selection and adaptive selection of parameters.

Description

technical field [0001] The invention belongs to the field of state detection and fault diagnosis of electric equipment, and in particular relates to a fault diagnosis method for an oil-immersed reactor based on an IFOA optimized SVM model. Background technique [0002] Reactor is an important power equipment in the power system to improve the power frequency voltage distribution of the line and improve the reactive power distribution of the line. The occurrence and operation reliability of its faults affect the safety and service life of the entire power system. Therefore, oil-immersed reactors It has important practical application value for fault diagnosis. [0003] In recent years, relevant research has applied many intelligent algorithms to reactor fault diagnosis, such as fuzzy clustering, genetic algorithm, artificial neural network, etc., but these algorithms require a large amount of fault sample data. Support vector machine (support vector machine, SVM) is a machin...

AI Technical Summary

Problems solved by technology

In the establishment of the SVM classifier, the selection of the kernel function g and the penalty factor C has a great impact on the diagnosis results, and there is randomness and blindness in the selection of model parameters

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