Transformer fault diagnosis improvement method based on dag-svm

Abstract

The invention discloses a transformer fault diagnosis lifting method based on a DAG-SVM. As for an acquired sample set for oil immersed transformers with class labels, each class is divided into training samples and testing samples according to a ratio of three to one; T new training samples whose number is smaller than the original training samples are extracted with placement from the training samples to train an SVM model, T weak learner are obtained, and a group of decision function sequence can be obtained; six class labels of normal state, medium temperature overheat, high temperature overheat, partial discharge, spark discharge and arc discharge and the acquired decision function sequence are used for building T DAG-SVM classification tree models; and the obtained T DAG-SVM classification tree models are used for fault diagnosis respectively. According to the transformer fault diagnosis lifting method based on the DAG-SVM disclosed by the invention, Bagging integration is carried out on a DAG-SVM algorithm, and the fault prediction precision is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of transformer fault online monitoring methods, and in particular relates to a DAG-SVM-based transformer fault diagnosis and promotion method. Background technique [0002] Oil-immersed transformers account for a large proportion of all types of transformers, and the operating status of transformers has a great impact on the safe operation of power systems. Therefore, it is very necessary to carry out fault diagnosis on oil-immersed transformers. [0003] Existing transformer fault diagnosis algorithms mainly involve methods such as artificial neural network, fuzzy set theory and gray system theory. These algorithms have great advantages but also have their own shortcomings. For artificial neural networks, such as: BP neural network algorithm has the disadvantages of slow convergence and easy to fall into local minimum points; for fuzzy set theory, such as: when fuzzy neural networks construct membership fu...

AI Technical Summary

Problems solved by technology

For artificial neural networks, such as: BP neural network algorithm has the disadvantages of slow convergence and easy to fall into local minimum points; for fuzzy set theory, such as: when fuzzy neural networks construct membership functions, there are human factors, which are easy to cause human errors; for Gray system theory, such as: the gray system correlation analysis method is too subjective, and it is difficult to determine the optimal value of some indicators

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