Partial discharge identification system for high‐voltage power transformers using fractal feature‐based extension method

Abstract

Partial discharge (PD) pattern identification is an important tool in high-voltage (HV) insulation diagnosis of power systems. Based on an extension method, a PD identification system for HV power transformers is proposed in this paper. A PD detector is used to measure the raw three-dimensional (3D) PD patterns of epoxy resin power transformers using an L sensor, according to which two fractal features (the fractal dimension and the lacunarity) and the mean discharge are extracted as critical PD features that form the cluster domains of defect types. The matter–element models of the PD defect types are then built according to the PD features derived from practical experimental results. The PD defect type can be directly identified by the correlation degrees between a tested pattern and the matter–element models. To demonstrate the effectiveness of the PD features extraction and the extension method, the identification ability is investigated on 144 sets of field-test PD patterns of epoxy resin power transformers. Compared with a multilayer neural network and K-means methods, the results show that a high accuracy together with a high tolerance in the presence of noise interference is reached by use of the extension method.