The genesis of the research work presented in this paper constitutes the issue of the effective and efficient recognition of single-source one-time partial discharge forms that can occur in insulation systems of power transformers. The paper presents research results referring to the use of single-direction artificial neural networks for recognizing basic partial discharge forms that can occur in paper-oil insulation impaired by aging processes. The research work results presented show the recognition effectiveness of basic partial discharge forms depending on the descriptor of the analysis of the acoustic emission signal analysis. The detailed cognitive aim was selection of input parameters and an artificial neural network which would be the best, considering recognition effectiveness and processing time, and which could be used as a classifier in an expert diagnostic system making identification of partial discharges measured by using the acoustic method possible.Index Terms -Partial discharge, paper-oil insulation, acoustics emission method, artificial neuron network, power transformer.
The paper presents the results of vibroacoustic investigations and the measurements of the acoustic pressure level (noise) of a transformer model. The measurements were taken at a full transformer load in two operation conditions: at a producer packeted core and at unpacketed core. Unpacketing consisted in loosing the screws pressing the particular core plates. Mechanical vibrations were registered and the acoustic pressure level was measured. The vibroacoustic analysis results are presented in the form of frequency spectra and the results of the acoustic analysis of the transformer under study are shown by determining corrected values of the acoustic pressure level and by a frequency analysis.
This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) G III type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.
This paper presents the preliminary results of the performed experiment, based on common phenomenon of partial discharges. A simple GeigerMuller counter (DP-66M) was used for measurement of a dose of ionizing radiation, which is accompanied by partial discharges. Values from which the radiation intensity was dependent could be controlled individually: the distance between measurer and source, as well as the voltage generating partial discharges. The obtained results indicate that in the examined phenomenon, signicant dose of X-ray radiation is present. According to the KramersKulenkamp theory, it depends also on the atomic number of elements involved. Using MATLAB software, the obtained data were compiled to develop suitable theory for further research study. There was also implemented fuzzication for the fuzzy logic, and the eect was the capability of forecasting radiation doses according to the xed variables for a given material, where partial discharges were generated.
The paper deals with mathematical modeling of acoustic emission signals which are generated in on load tap changers. In power engineering area acoustic emission method is commonly used for diagnosis purposes. Authors in their research works apply it for detection of defects occurring in on load tap changers, which are important elements of power transformers. The acoustic emission method is based on measurements performed by use of wideband piezoelectric transducers that are mounted on transformer tank surface. The registered signal is then analyzed in the time, frequency, and timefrequency domains or is evaluated with computer expert systems for diagnosis purposes. Depending on indicators device technical condition can be determined. The contribution given by the authors in this paper is proposal of a mathematical model, which describes envelope of the measured acoustic emission signal.
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