Application of genetic algorithms to pattern recognition of defects in GIS

A computerized pattern recognition system based on the analysis of phase resolved partial discharge (PRPD) measurements, and utilizing genetic algorithms, is presented. The recognition system was trained to distinguish between basic types of defects appearing in gas-insulated system (GIS), such as voids in spacers, moving metallic particles, protrusions on electrodes, and floating electrodes. The classification of defects is based on 60 measurement parameters extracted from PRPD patterns. Classification of defects appearing in GIS installations is performed using the Bayes classifier combined with genetic algorithms and is compared to the performance of the other classifiers, including minimal-distance, percent score and polynomial classifiers. Tests with a reference database of more than 600 individual measurements collected during laboratory experiments gave satisfactory results of the classification process