The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established.

This article concerned on the mechanism of functioning of two multi-comparational algorithms, that is: CMCA (Classic Multi-Comparational Algorithm) and OMCA (Optimised Multi-Comparational Algorithm). The range and possibility of application of frequency descriptors that explicitly characterise the acoustic emission (AE) signals from partial discharges (PD) were described. The method for selection of quantity of the standard waveforms for the purpose of the classification process, carried out using the tested algorithms was presented. In addition, there were showed results of the simulation, performed using the proposed algorithms and two databases containing the AE signal waveforms generated by basic forms of PD, which can occur in insulation systems of transformers. The efficiency of CMCA and OMCA algorithms in application for AE signals generated by PD, as well as the reason to complete the developed database by another class of waveforms due to the defect of insulation system that not being catalogued previously was demonstrated in the study.

The paper presents the application of the multicomparative algorithm for classifying acoustic signals generated by eight basic partial discharge (PD) forms modeled in insulation oil. The signals were measured and catalogued by using the acoustic method. The aim of the research work carried out was creation of a discriminating classifier which would make it possible to recognize the eight basic signal classes which could be associated with a strictly defined defect type of paper-oil insulation of power transformers. (The application of the multicomparative algorithm for classifying acoustic signals coming from partial discharges).

Application of the optimized multi-comparational classification algorithm for acoustic emission signals generated by eight general forms of partial discharges, modeled in insulation oil are presented in the paper. Moreover results of classification effectiveness calculated by use of this algorithm considering eight hundred runs analyzed for time-frequency descriptor in its five variants are presented. (The classification of acoustic signals from partial discharge using the optimized multi-comparison algorithm and time-frequency descriptor)

The paper presents the results of a comparative analysis of electrical parameters of the Testa transformer, which were determined analytically and obtained experimentally. To this end, structural and design calculations were carried out, which were confronted with the results of the measurements taken of the appliance in operation.

This experimental research work attempts to observe the influence of the electromagnetic radiation of the frequency of 2.45GHz on ionized gases coming from the process of burning of three various types of fuel. A digital camera was used for the registration of the phenomena taking place during the experiments.

This paper presents the field-circuital model of the Tesla transformer made by the use of the Maxwell program. As a result of this the magnetic field distribution around active parts of the transformer and its values were obtained. Current density distributions inside current circuits of the computer simulations carried out were presented.