Method for recognizing transformer partial discharge pattern based on singular value decomposition algorithm

摘要

A method for recognizing a transformer partial discharge pattern based on a singular value decomposition (SVD) algorithm includes a training model and a classification recognizing process, comprising: firstly setting up an experimental environment having artificial defects, collecting at least one datum sample, and calculating statistical feature parameters of each datum sample to form a datum sample matrix; performing singular value decomposition on the datum sample matrix and determining an order of an optimal retention matrix by judging whether a feature of a retention matrix is clear, so as to obtain a type feature description matrix and a class-center description vector group after dimensionality reduction; preprocessing samples to be recognized to obtain a sample vector, and performing linear transformation on the sample vector utilizing a type space description matrix.

主权项

1. A method for recognizing a transformer partial discharge pattern based on a singular value decomposition algorithm, comprising following steps of:
step (1) setting up an experimental environment having multiple discharge patterns and artificial defects, and collecting at least one sample datum of partial discharge related measurement parameter; step (2) calculating statistical feature parameters of the sample datum of related measurement parameter of partial discharge collected in the step (1); step (3) forming a training sample matrix and a testing sample matrix, wherein composition structure of the training sample matrix and the testing sample matrix is the same, each row of the training sample matrix and the testing sample matrix is the statistical feature parameter, and each column thereof is a sample; step (4) performing singular value decomposition on the training sample matrix and determining an optimal order of a retention matrix; step (5) forming a classification model according to a sample matrix obtained by the singular value decomposition, wherein the classification model is formed by a type feature space description matrix and a class-center description vector group; and step (6) preprocessing the testing sample matrix or on-site collected samples to be classified to obtain a sample vector to be classified, and performing classification recognizing.