Investigation on Using Fractal Geometry for Classification of Partial Discharge Patterns

Classification of Partial discharge (PD) patterns is an important tool to determine the sources of the PD. Also the features that specify of the patterns can be used monitor the condition of the electrical insulation. A PD patterns classification approach of artificial partial discharge sources by using neural networks is proposed in this paper. The classification process was based on features generated from threedimension PD patterns. These features were obtained with the aid of fractal geometry. The box counting technique is used to generate the fractal features (fractal dimension and lacunarity). Each PD pattern has a unique Fractal dimension and several values for lacunarity depends upon the size of the box. Therefore each PD pattern is characterized by two features but with several quantities. The number of input features and their contents of information determine the performance of neural networks based classification system. In this paper an attempt to improve the performance of PD classification systems by minimizing the number of the input features was introduced. This was done by investigating the ability the different lacunarity values to classify different PD sources. A wide range of box size was investigated. The obtained results show that the lacunarity values generated from a very narrow range of box sizes has the maximum ability for PD classification.