Power transformer fault diagnosis method and device

Abstract

The invention discloses a power transformer fault diagnosis method and device. The method comprises the steps that: after dissolved gas analysis (DGA) data in power transformer oil is obtained, faulttype identification on a fault sample is carried out through a support vector regression-variable prediction model (SVR-VPMCD); firstly, ratios of all kinds of gas in the total gas content are selected as various characteristic values, so that corresponding characteristic vectors are formed; then, polynomial response surface regression in VPMCD is replaced by SVR; variable prediction models (VPMs)corresponding to various fault types are constructed by utilization of a training sample; finally, each test sample is predicted by sequential utilization of the constructed VPMs of all kinds of faults; and the fault type corresponding to the VPM having the minimum prediction error is the fault type of the test sample. The SVR-VPMCD method sufficiently considers mutual correlation among various characteristic values, and overcomes the disadvantage that the VPMCD method has poor processing capability to high-dimensional data; therefore, the identification precision of the mode identification method on the sample type is increased; and the method has good small sample data identification capability.

Description

technical field [0001] The present application relates to the field of high-voltage electricity, and more specifically, to a power transformer fault diagnosis method and device. Background technique [0002] The transformer is an important equipment in the power system, and its operating status directly affects the safety level of the system. With the development and gradual popularization of transformer condition monitoring, it is necessary and feasible to establish a fault diagnosis system based on transformer condition online monitoring information. [0003] Since the composition and content of dissolved gas in transformer oil can reflect the operating state of the transformer to a large extent, the Dissolved Gas Analysis (DGA) method has become an effective method for fault diagnosis of oil-immersed transformers. And on this basis, artificial intelligence methods such as the three-ratio method, Rogers, and artificial neural network (ANN), Bayesian classifier and support...

AI Technical Summary

Problems solved by technology

However, the existing classification methods of transformer fault types often ignore the intrinsic relationship between each feature value, and the ability to identify small sample data is poor

In addition, traditional recognition methods cannot identify samples belonging to discharge types that are not included in the training sample set

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