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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.Index Terms -Partial discharge, paper-oil insulation, acoustics emission method, artificial neuron network, power transformer.

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Analyses of acoustic wave propagation in a model power transformer are presented in the paper. The acoustic wave is induced by partial discharges that are simulated at predefined coordinates in the core and winding. Propagation of the numerical calculated acoustic wave is analyzed within the transient state. Achieved results indicate that the space and time distributions of the acoustic pressure depend on the induction position. Furthermore, a greater pressure gradient is observed in domains with higher speed of sound while the largest amplitude occurs at the vicinity of the induction position.