This paper presents the effect of the impact of moisture in paper insulation used as insulation of transformer windings on its thermal conductivity. Various types of paper (cellulose and aramid) and impregnated (mineral oil, synthetic ester, and natural ester) were tested. The impact of paper and impregnated types on the changes in thermal conductivity of paper insulation caused by an increase in moisture were analyzed. A linear equation, describing the changes in thermal conductivity due to moisture, for various types of paper and impregnated, was developed. The results of measuring the thermal conductivity of paper insulation depending on the temperature are presented. The aim of the study is to develop an experimental database to better understand the heat transport inside transformers to assess aging and optimize their performance.

The power transformer plays an important role in electric power systems. One of the conditions for the proper operation of the transformer is to ensure a sufficiently low temperature. This condition can be met if the heat exchange is effective. Heat transfer depends, among other things, on the electrically insulating liquid. The thermal property describing the ability of a liquid to transfer heat is the heat transfer coefficient α. At the design stage of the transformers, it is most often assumed that the value of the α coefficient is constant and equal to 100 W·m⁻²·K⁻¹. Such simplifications can lead to the improper design of the transformer since this factor depends on many factors. The article presents the results of research on the dependence of the heat transfer coefficient α on the type of electrical insulation liquid, the thermal load of the cooled surface, and the length of the heating element. Four types of electrical insulating liquids were considered: mineral oil, synthetic ester, natural ester, and natural ester with reduced viscosity. The obtained results prove that the type of electrical insulating liquid and the thermal surface load value affect the α coefficient. The length of the heating element did not affect the α factor.

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.

The research reported in this paper involves the development and refinement of methods applicable to the measurement and analysis of infrasound signals generated by the operation of wind turbines. In particular, the presentation focuses on the use of a new system that is applied for simultaneous recording of acoustic signals in the low-frequency range emitted by wind farms in three independent and identical measurement setups. A comparative analysis of the proposed new system was made with the Brüel & Kjaer measurement, a commonly used methodology, which meets the requirements of the IEC 61400-11 standard. The paper focuses on the results of frequency and time-frequency analysis of infrasound signals recorded throughout the operation of a wind turbine with a rated capacity of 2 MW. The use of a correlated system with three simultaneous measurement systems can be a new and alternative measurement method that will eliminate the drawbacks of previous approaches.

In high voltage insulation systems, dielectric materials may be exposed to partial discharges (PD), which can lead to equipment failures and safety hazards. Therefore, it is crucial to detect and characterize PD activity on the surface of insulation systems. Techniques such as radio frequency signal analysis and ultraviolet radiation emission detection are commonly used for this purpose. In this research study, an analysis was conducted on the signals emitted by surface PD in the radio frequency and ultraviolet radiation emission ranges. The goal was to indicate possible directions for further basic research aimed at building a knowledge base and improving measurement methods. The analysis confirmed that radio frequency and ultraviolet signal analysis can provide important information about the activity and location of PD on the surface, including the intensity and nature of PD. The experimental investigation presented in this paper provides valuable insights into the potential for using radio frequency and ultraviolet signals to enhance diagnostic techniques for monitoring the condition of insulation systems in high-voltage equipment.

The development of wind energy and the increasing number of installed wind turbines make it necessary to assess them in terms of the nuisance of the emitted infrasound noise generated by such devices. The article presents the results of measurements and analyses of infrasound emitted during the operation of wind turbines installed in various locations in Poland. Comparative analysis of noise levels in the infrasound and audible range has shown that acoustic energy is mainly in the low and infrasound frequency range, and the measured levels depend significantly on the weighting curves used. On the basis of the results, it was confirmed that the sound pressure level of infrasound signals emitted by the operation of high-power wind turbines, regardless of wind velocity, weather conditions, design solutions of turbines, operating time, rated capacity, does not exceed the criteria specified in the applicable legislation dealing with the assessment of infrasound noise on the working environment.