Transformer fault diagnosis method based on integrated learning Bagging algorithm

Abstract

The invention discloses a transformer fault diagnosis method based on integrated learning Bagging algorithm in the technical field of transformer fault diagnosis. In the method, with a ball vector machine (BVM) as a weak learning algorithm of the Bagging algorithm, the dissolved gas analysis (DGA) technology as a data acquiring method, a sample set suitable for the BVM is obtained through data processing methods such as data normalization, category numeralization and the like; the weak learning algorithm is repeatedly invoked to train the sample set in the integrated learning Bagging algorithm comprises so as to obtain a strong learning machine H; the strong learning machine H is used as a transformer fault diagnostic model to judge the fault of a to-be-diagnosed record. The transformer fault diagnosis method has good adaptability and low diagnosis error in terms of improving the transformer fault diagnosis precision.

Description

technical field [0001] The invention belongs to the technical field of transformer fault diagnosis, in particular to a transformer fault diagnosis method based on a Bagging algorithm. Background technique [0002] With the rapid development of smart grid technology, the traditional transformer fault diagnosis technology has undergone tremendous changes, and more and more intelligent methods have been introduced into the field of fault diagnosis. As a common power equipment, transformer has a relatively complex structure and various types of faults. How to quickly and accurately diagnose transformer faults has strong theoretical and practical significance for improving its operation and maintenance level and power system security. At present, the transformer fault diagnosis method mainly uses the single classifier method in the field of pattern recognition, and the hydrogen gas H at each time monitoring point in the target transformer collected by the dissolved gas analysis t...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the shortcomings of the single-classifier-based fault diagnosis technology mentioned in the above-mentioned background technology, which are prone to over-fitting phenomenon and cause large diagnostic errors and complicated calculation processes, the present invention proposes a transformer fault diagnosis technology based on ensemble learning Bagging algorithm. diagnosis method

Images