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Frequency Domain Spectroscopy (FDS) is an effective tool allowing assessing the condition of oil-paper insulation system in power equipment. However, results from these measurements are known to be greatly influenced by various parameters, including insulation aging, moisture content, and insulation geometry/volume, together with environmental condition such as temperature. In this contribution, a series of experiments have been performed under controlled laboratory conditions. The dielectric response of the oil impregnated paper, along with the degree of polymerization and moisture content, were monitored. Since dielectric parameters are geometry dependent, poles (independent of the geometry) which depends on resistivity and permittivity, were considered to assess the condition of the insulation. From the investigations performed on new and aged samples, it is shown that poles (P) can be regarded as insulation aging indicator. It is also shown that a per unit value based on the Dielectric Dissipation Factor (DDF), measured in the frequency range from 1 to 1000 Hz can be correlated to moisture content in the insulation paper.

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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.

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.Index Terms -Power transformer, neural networks, FDS, power factor, dielectric dissipation factor, paper oil insulation.

Abstract:It is now well-established that moisture in the oil paper insulation used in power and instrument transformers significantly reduces the transformers' lifetimes, and can eventually lead to premature failure. This moisture should, therefore, always be removed, not only during production but also after repairs. At the final stage of manufacturing, the drying process should be carried out to remove water and air vacuoles contained in the cellulose-based paper before impregnation. Successful drying helps increase the residual life of transformers, because the presence of moisture and air vacuoles accelerates the aging/degradation process of the oil paper insulation. Proper estimation of residual moisture before impregnation and the determination of the time required for drying play key roles in the time-consuming process of drying. In this paper, the disadvantages of inadequate drying are addressed, followed by a mathematical approach to model the paper drying process. A mathematical model describing the kinetics of drying according to temperature, initial moisture, paper weight, final moisture, and extraction rate is proposed. This model also estimated the amount of moisture removed at the end of the drying process.