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This paper presents a study of the impact of two important parameters, moisture and aging of the oil/paper dielectric used as insulation in power transformers.The way in which these two parameters influence different parameters of the Frequency Domain Spectroscopy (FDS) measurements, is emphasized.Different FDS parameters were measured by varying the moisturecontent and the aging degree of the oil impregnated paper.The use of two types of neural networks for analysis of the results was necessary in order to help discriminating the impact of moisture and aging on the FDS measurements and, in some cases, to estimate the aging duration of the paper impregnated with oil. [ABSTRACT FROM PUBLISHER]

This paper presents the analysis of leakage current evolution of an ice-covered station post insulator during a melting period using artificial neural network (ANN) models. The tests, carried out under wet-grown ice regime for different experimental conditions, showed that the permanent establishment of white arcs, identified as "permanent regime" led to flashover in the large majority of the cases,. Based on these observations, the development of a monitoring methodology aimed at forewarning the approach of the leading white arc during melting periods is proposed. The monitoring methodology uses different ANNs in order to predict the appearance of the white arc based on the identification, classification and analysis of the occurrence frequency of electric discharges. The results show that the ANN monitoring model developed is able to predict the onset of permanent regime under various experimental conditions. Hence, it was found that the delay between the permanent regime onset prediction delivered by the ANN model and its realization is 9 minutes on average. These results confirm that the proposed ANN model could be used as part of a monitoring system for post insulators during icing events for protection against potential flashover hazards. [ABSTRACT FROM AUTHOR]