Transformer fault diagnosis method based on feature information quantization and weighted KNN

Abstract

The invention discloses a transformer fault diagnosis method based on feature information quantization and weighted KNN, comprising the following steps: S1, dividing sample data into a training set and a test set; S2, inputting the training set, and performing preprocessing on the sample data; S3, based on principal component analysis (PCA) and grey relational analysis (GRA), performing quantization on fault feature information; S4, introducing a particle swarm optimization algorithm for optimizing a weighted KNN categorization algorithm, according to a true fault category, training a sample in a standardized fault feature matrix, and obtaining a power transformer fault diagnosis model, thus categorization on a power transformer fault is realized; and S5, inputting the test set into the power transformer fault diagnosis model, and obtaining a diagnosis result, thus diagnosis on the power transformer fault is realized. The transformer fault diagnosis method disclosed by the invention solves the problems that processing efficiency is low, model training is difficult and limitation exists in the prior art.

Description

technical field [0001] The invention belongs to the technical field of power faults, in particular to a transformer fault diagnosis method based on feature information quantization and weighted KNN. Background technique [0002] As one of the core equipment in the power system, the power transformer takes effective measures to make accurate judgments on the abnormal state or fault inside the transformer, which is of great significance to the whole system. Power transformer fault diagnosis methods are mainly divided into three categories: the first type is based on analytical models, through the establishment of precise mathematical and physical models for transformer fault diagnosis; the second type is the use of incomplete prior knowledge to establish qualitative models, reasoning Transformer fault categories, such as expert systems, fault decision trees and other methods; the third category is based on data-driven power transformer fault intelligent classification methods,...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a transformer fault diagnosis method based on characteristic information quantification and weighted KNN with high classification and diagnosis efficiency, high practicability and high accuracy, which solves the problems existing in the prior art Low efficiency, difficult model training and limitations

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