Application Possibilities of Artificial Neural Networks for Recognizing Partial Discharges Measured by the Acoustic Emission Method

The genesis of the research work presented in this paper constitutes the issue of the effective and efficient recognition of single-source one-time partial discharge forms that can occur in insulation systems of power transformers. The paper presents research results referring to the use of single-direction artificial neural networks for recognizing basic partial discharge forms that can occur in paper-oil insulation impaired by aging processes. The research work results presented show the recognition effectiveness of basic partial discharge forms depending on the descriptor of the analysis of the acoustic emission signal analysis. The detailed cognitive aim was selection of input parameters and an artificial neural network which would be the best, considering recognition effectiveness and processing time, and which could be used as a classifier in an expert diagnostic system making identification of partial discharges measured by using the acoustic method possible.