PD recognition with knowledge-based preprocessing and neuralnetworks

Partial discharge (PD) patterns are an important tool for the diagnosis of HV insulation systems. Human experts can discover possible insulation defects in various representations of the PD data. One of the most widely used representations is phase-resolved PD (PRPD) patterns. We present a method for the automated recognition of PRPD patterns using a neural network (NN) for the actual classification task. At the core of our method lies a preprocessing scheme that extracts relevant features from the raw PRPD data in a knowledge-based way, i.e. according to physical properties of PD gained from PD modeling. This allows a very small NN to be used for classification. In addition to the classification of single-type patterns (one defect) we present a method to separate superimposed patterns stemming from multiple defects. High recognition rates are achieved with a large number of single patterns generated by stochastic PD simulations. Our network architecture compares favorably with a more traditional network architecture used previously for PRPD classification. These results are confirmed by classification of patterns measured in laboratory experiments and power stations