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%.
This work concerns the concept and verification of the experimental possibility of using a wavelet transform to assess a steel structure’s condition. In the research, a developed measuring stand was used. Mechanical waves in the metal plate were excited by the impact. These waves were recorded with an electroacoustic transducer and registered in the form of electrical signals. Both the signals generated by the actuator of the plate and the signals reaching the transducer were recorded. The registered data were decomposed into wavelet coefficients. Laboratory tests have shown the possibility of applying this type of test to identify damage in steel structural elements—the relationship between the details of the wavelet transform and the type of damage was demonstrated.
The paper presents results of research of electrical treeing of solid dielectrics with the method of acoustic emission (AE). The study was performed with an alternating voltage of 50 Hz in the range up to 21 kV (RMS) on methyl polymethacrylate or crosslinked polyethylene samples. They were of cuboidal shape of the dimensions 25 × 10 × 4 mm. One of the cuboid sample walls of the dimensions 25×4 mm was covered with a conducting paint. On the opposite wall, a surgical needle of T-25 type was screwed. The distance between the electrodes (the needle and the wall covered with a conducting paint) was in the range 0.5-2.0 mm. Registered signals were denoised with wavelet transformation method and then there were analyzed. The following parameters were analyzed: a sum and rate of acoustic emission counting, a sum and rate of acoustic emission events, RMS value of the electric signal leaving the converter. Spectrum and spectrogram were also analyzed. It was found that AE signals are generated during electrical treeing of solid dielectrics. Values of chosen parameters increased their values when the process begins. There are also some dominant frequencies ranges, different for different kinds of dielectrics, connected with the treeing.
Abstract. Two variants of electricity transmission from the Transfer-Switching Station (TSS) to the battery charging station were analysed in the paper. The first variant under analysis referred to the transmission of electricity via a three phase a.c. line with rated voltage of 15 kV. In the second variant of electricity transmission, the d.c. line with the working voltage of 1500 V was used. For both variants, lines and other equipment such as transformers, rectifier system and voltage stabilisation system in the battery charging station were modelled. For both solutions, analysis of energy per annum was conducted depending on the distance of the charging station from the TSS. On top of this, the simultaneous operation of several chargers was taken into account, which would correspond to the case of charging many buses at the same time from a single power line. The paper demonstrates that in the case of the analysed electricity transmission systems in the electric bus battery charging systems it is possible to use a more advantageous solution, which is characterised by reduced power and energy losses.
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.
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