Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%.

Based on a method to reduce energy consumption suggested in a real energy audit carried out in an industrial plant located in Poznań (city in Poland), the potential of using photovoltaic (PV) panels as wall cladding was analyzed, in order to reduce energy (electric and thermal) consumption and financial expenditure. The authors’ concept of using building integrated photovoltaic installation (BIPV) was presented and tested. This study checked whether the presence of PV modules would also affect heat transfer through the external wall of the building on which the installation is located. The analysis consisted of determining, for two variants, the heat transfer coefficients across the partition, in order to estimate the potential thermal energy savings. The first variant concerned the existing state, i.e., heat transfer through the external wall of the building, while the second included an additional partition layer in the form of photovoltaic panels. As a result, the use of panels as wall cladding allowed the improvement of the thermal parameters of the building wall (by increasing the thermal resistance of the wall), and the reduction of gas consumption for heating. The panels also generate electricity for the factory’s own needs. Payback time, compared to calculations which do not include changes in thermal parameters, was shortened from 14 to 11 years. The main reason for this is that gas consumption is reduced due to the improved heat transfer coefficient of the wall and the reduction of the heat loss of the facility. This aspect is usually overlooked when considering photovoltaic installations and, as argued by this paper, can be important.

The paper presents a computer application developed for analysis of acoustic emission signals accopanying the process of electric treeing occurring in polymeric materials, and in particular epoxy resins used as high-voltage solid insulation. A method for measuring, recording and registration of signals was presented. In particular,it is shown how the developed application uses wavelet transformation to remove noise from the recorded signal, and to carry out a time-frequency analysis of these signals. The results of the sample analysis of the recorded signals were also shown. Streszczenie. W artykule zaprezentowano aplikację komputerową opracowaną w celu analizy sygnałów emisji akustycznej towarzyszących procesowi drzewienia elektrycznego materiałów polimerowych, w szczególności żywic epoksydowych stosowanych jako wysokonapięciowa izolacja stała. Zaprezentowano sposób pomiaru, rejestracji i rejestracji sygnałów. W szczególności pokazano w jaki sposób opracowana aplikacja wykorzystuje transformację falkowa do usunięcia szumów z zarejestrowanego sygnału, a także do prowadzenia analizy czasowo-częstotliwościowej tych sygnałów. Pokazano również wyniki przykładowej analizy zarejestrowanych sygnałów. Analiza sygnałów emisji akustycznej towarzyszących procesowi drzewienia elektrycznego materiałów polimerowych Słowa kluczowe: transformata falkowa, drzewienie elektryczne; badania nieniszczące, izolacja wysokonapięciowa.

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One of the challenges which the electrical power industry has been facing nowadays is the adaptation of the power system to the energy transition which has been taking place before our very eyes. With the increasing share of Renewable Energy Sources (RES) in energy production, the development of electromobility and the increasing environmental awareness of the society, the power system must constantly evolve to meet its expectations regarding a reliable electricity supply. This paper presents the issue of deploying energy storage facilities in the meshed power distribution network in order to reduce transmission losses. The presented multi-objective approach provides an opportunity to solve this issue using multi-objective optimisation methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Particle Swarm Optimization (MPSO) and Biased Random Keys Genetic Algorithm (BRKGA). In order to increase the efficiency optimisation process, the Pareto Adaptive ϵ-dominance (paϵ-dominance) was used. It was demonstrated that the use of energy storages that cooperate with RES can significantly reduce transmission losses.