PD recognition by means of statistical and fractal parameters and a neural network

A novel partial discharge (PD) defect identification method is described. Starting with PD data on different families of specimens, a suitable set of parameters are determined and then used as input variables to a neural network for the purpose of identifying the defects within the insulation. In this procedure the statistical Weibull analysis is performed on PD pulse amplitude histograms to obtain the scale parameter /spl alpha/ and the shape parameter /spl beta/. Thereafter, the two statistical operators (skewness and kurtosis) and two fractal parameters (fractal dimension and lacunarity) are evaluated from the PD phase on the discharge epoch histogram and from the 3 dimensional (pulse amplitude/phase/discharge rate) histogram, respectively. Following the exposition of the basic mathematical concepts regarding the above parameters, experimental results are reported on the recognition capability of the method in defining the defect category in a number of different specimens.