Partial discharge diagnosis method based on naive bayesian classification

Abstract

The invention discloses a partial discharge diagnosis method based on naive bayesian classification. The method includes the steps that data acquisition is performed, and then, acquired signals are subjected to anti-interference processing; the processed signals are converted into a two-dimensional data group; a PRPD map, an N-P map and a Q-P map are respectively obtained according to a PRPS map; feature extraction is performed on the PRPD map, the N-P map and the Q-P map respectively; an equal-width discretization method or an equal-frequency discretization method is used for performing discretization on data having been subjected to feature extraction; a partial discharge fault category is obtained through naive bayesian classification. The method has the advantages that the correction rate is 80.5%, and the on-site actual application requirement can be met. Meanwhile, the equal-width non-supervision discretization method and the equal-frequency non-supervision discretization method are researched in detail, it shows that the equal-frequency discretization method is superior to the equal-width discretization method, and the optimal empirical value of the equal-frequency discretization method is given.

Description

technical field [0001] The invention relates to the technical field of automatic control, in particular to a partial discharge diagnosis method based on naive Bayesian classification. Background technique [0002] Partial discharge refers to the generation of electricity that occurs in localized areas of high voltage equipment insulation due to uneven electric fields. Due to the repetitiveness of partial discharge, although it will not affect the normal operation of the equipment in a short period of time, it will form an electrical tree branch after a long time, eventually leading to insulation breakdown. Because partial discharge is accompanied by pulsed electric waves, ultrasonic waves, electromagnetic waves, chemical reactions and luminescence, the corresponding partial discharge detection methods include traditional partial discharge measurement, ultrasonic detection method, UHF detection method, chemical method and photometric method. [0003] Many scholars at home an...

AI Technical Summary

Problems solved by technology

For the problem of partial discharge fault diagnosis, signal reception and data processing have a great impact on the classification of the classifier, and the lack of overall guidance will have a certain impact on the diagnosis results

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