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.

This paper concerns the application of the AHP (analytic hierarchy process) multi-criteria decision support method for the final selection of the structure of a hybrid power system with RESs (renewable energy sources) and EES (electrical energy storage) from a set of solutions obtained through multi-criteria optimization. These solutions, depending on their position within the Pareto front, may differ significantly in terms of the values of the criteria functions, or may be located very close to each other in the solution space. The role of the expert is to select the final solution, taking into account many additional criteria, often of a subjective nature. The article optimizes the structure of the proposed system using the multi-criteria NSGA-II (Non-dominated Sorting Genetic Algorithm) method, taking into account three technical criteria. The AHP method was used to select the final solution, which allows determination of the ranking of solution variants, taking into account selected additional criteria. In the analyzed case, these are primarily economic indicators, technical conditions, and preferences of the system recipients. In addition to determining the ranking of solutions, a sensitivity analysis was performed, which gives the expert extensive knowledge on the impact of individual criteria on the order of variants in the ranking. It was shown that in the case of selecting hybrid structures of generating systems with EES for a specific type of receiver, the use of the AHP method significantly facilitates making the final decision.

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.

This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

Niniejsza praca dotyczy zapewnienia zasilania w energię elektryczną budynku (domku) letniskowego. Wykorzystano do tego celu odnawialne źródła energii w postaci turbiny wiatrowej oraz paneli fotowoltaicznych współpracujących z magazynem energii w celu zbilansowania produkcji oraz zapotrzebowania na energię. Na podstawie danych meteorologicznych i szacowanego profilu obciążenia wykonano symulację pracy całego układu, co pozwoliło na ocenę pracy instalacji w zakresie technicznym oraz ekonomicznym.