To realize accurate pattern recognition of partial discharge in electrical equipment

a partial discharge pattern recognition method based on the kernel entropy component analysis of time-frequency feature was proposed. In this method

the time-frequency analysis of partial discharge pulse signal was carried out using the S-transform theory. Considering difficult pattern recognition of the S-transform due to a large number of dimensions of analysis results and too much redundant information

the kernel entropy component analysis（KECA） was adopted to compress the S-transform results. The time-frequency feature vector was obtained to recognize partial discharge type accurately by means of the random forest classifier. Furthermore

four typical transformer insulation defect models

which can produce point discharge

surface discharge

bubble discharge and suspended discharge

were set up in the laboratory

and the partial discharge signals were collected. Partial discharge pattern recognition experiments were conducted with the proposed method

PCA method and KPCA method

respectively

and the results showed that the proposed method obtained higher pattern recognition accuracy with shorter time.