A heuristic complex probabilistic neural network system for partial discharge pattern classification

Abstract

Partial discharge (PD) pattern classification has recently become popular since the automated acquisition of PD signals has become vital and cogent. A novel method for identification of defects due to partial discharge is described in this paper. Starting from different PD families of specimen, several sets of characteristic vectors are determined and then used as input variables to the proposed neural network. The innovative trend of using probabilistic neural network (PNN) towards classification of PD patterns is coherent and perceptible. The paper elucidates the structure of PNN, which has been appropriately customized for determining the optimum value of smoothing parameter. PD is measured using the conventional discharge detector and previously developed statistical tools that processed the PD patterns. Satisfactory results in the past have revealed that the analysis of the properties of the phase position distributions can be made using mathematical descriptors. The ability of PNN to classify these descriptors in addition to classifying the inputs derived from the measures based on central tendency, dispersion, and maximum and minimum values are investigated. The classification of single-type insulation defects has been envisaged. The paper also expounds a novel complex technique adopted for precise PD classification.