Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring complex shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for an image surface, fractal dimension and lacunarity have been found to be useful for recognition purposes. Partial discharges (PD) occurring in all HV insulation systems is a very complex phenomenon, and more so are the shapes of the various 3-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and quantify these pattern shapes must be explored, before recognition systems based on them could be developed. This contribution reports preliminary results of such a study, wherein the 3-d PD pattern surface was considered to be a fractal, and the computed fractal features (fractal dimension and lacunarity) were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made.
