A New Method Based on Extension Theory for Partial Discharge Pattern Recognition

This paper proposes a new partial discharge (PD) pattern recognition method base on the extension theory. First, five types of defect models are well-designed on the base of investigation of many power equipment failures. A commercial PD detector is used to measure the three-dimension (3D) PD patterns, then two fractal features (fractal dimension and lacunarity) and mean discharges of phase windows are extracted from the raw 3D PD patterns. Second, the matter-element models of the PD defect types are built according to the PD features derived from practical experimental results. The PD defect type can be directly identified by correlation degrees between the tested pattern and the matter-element models. To demonstrate the effectiveness of the proposed method, comparative studies using a multilayer neural network (MNN) are conducted on 200 sets of field-test PD patterns with rather encouraging results. Key-Words: Extension Theory, Partial Discharge, Pattern Recognition.