Partial discharge is a well-known symptom of insulation degradation in power equipment.
It is, therefore, of great importance to identify the origin and the number of partial discharge sources to obtain early warning about an imminent failure. Automated recognition of partial discharges has long been a subject of numerous investigations. A number of approaches appeared over the years. Statistical parameters, fractal features and Fourier coefficients in combination with conventional or neural network classifiers have all been used for this purpose. So far success has been achieved only in some specific situations, for example, when recognising single discharging defects.