The paper presents the results of measurements of electrical strength of Midel 1204 natural ester doped with iron nanopowder in a hydrophobic carbon shell. The research was conducted for different concentrations of the dopant. The samples were prepared in the High Voltage Technique Laboratory. After mixing, they were tightly closed, and the first measurements were taken after 5 weeks of dissolution of the dopant in liquid. The tests were repeated after another 2 weeks and 3 weeks of dissolution of nanoparticles. An increase in both mean and maximum breakdown voltage was shown for the tested liquid mixtures. The concentration for which the value of electrical strength begins to decrease was indicated. It was also shown that a longer time of dissolution of nanoparticles causes an increase in the electric strength value for the tested samples.
Classification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. The data that were analyzed concerned ionizing radiation signals generated by partial discharges, recorded by a method using the phenomenon of scintillation. It was decided to check if the data could be classified and if it was possible to determine the defect of an electrical power device. It was possible to find out which classifier (algorithm) worked best for the task, and that the data obtained can be classified, as well as that it is possible to determine the defect. In addition, it was possible to check what effect changing the default values of the classifier’s parameters has on the effectiveness of classification.
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