Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 5
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

This paper presents a method for estimation of core losses in banks of single phase power transformers that are subjected to an injected DC current such as geomagnetically induced currents (GIC). The main procedure of the core loss calculation is to obtain a magnetic flux density waveform in both time and location by using a novel algorithm based on 3D FEM inside the core and then to calculate the loss distribution based on loss separation theory. Also, a simple and effective method is proposed for estimation of losses of asymmetric minor loops by using combination of symmetric loops. The effect of DC biasing on core losses in single phase power transformers is investigated and the sensitivity of core type and material is evaluated. the results shows that DC current biasing could increase core losses up to 40 percent or even more.
Go to article

Authors and Affiliations

Seyed Mousavi
Göran Engdahl
Edris Agheb
Download PDF Download RIS Download Bibtex

Abstract

This paper presents comparative analysis of various acoustic signals expected during partial discharge (PD) measurements in operating power transformer. Main purpose of the paper is to yield relevant and reliable method to distinguish between various acoustic emission (AE) signals emitted by PD and other sources, with particular consideration of real-life results rather than laboratory simulations. Therefore, selected examples of real-life AE signals registered in seven different power transformers, under normal operation conditions, within few years are showed and analyzed. Five scenarios are investigated, which represent five types of AE sources: PD generated by artificial sources, and next four real-life sources (including PD in working transformer, oil flow, oil pumps and core). Several different signal processing methods are applied and compared in order to identify the PD signals. As a result, an energy patterns analysis based on the wavelet decomposition is found as the most reliable tool for identification of PD signals. The presented results may significantly support the process of interpretation of the PD measurement results, and may be used by field engineers as well as other researchers involved in PD analysis using AE method. Finally, observed properties also provide a solid basis for establishing or improving complete classification method based on the artificial intelligence algorithms.

Go to article

Authors and Affiliations

Michał Kunicki
Download PDF Download RIS Download Bibtex

Abstract

The paper presents the results of the application of the hierarchical clustering methods for the classification of the acoustic emission (AE) signals generated by eight basic forms of partial discharges (PD), which can occur in paper-oil insulation of power transformers. Based on the registered AE signals from the particular PD forms, using a frequency descriptor in the form of the power spectral density (PSD) of the signal, their representation in the form of the set of points on plane XY was created. Next, these sets were subjected to analysis using research algorithms consisting of selected clustering methods. Based on the suggested numeric performance indicators, the analysis of the degree of reproduction of the actual distribution of points showing the particular time waveforms of the AE signals from eight adopted PD forms (PD classes) in the obtained clusters was carried out. As a result of the analyses carried out, the clustering algorithms of the highest effectiveness in the identification of all eight PD classes, classified simultaneously, where indicated. Within the research carried out, an attempt to draw general conclusions as to the selection of the most effective hierarchical clustering method studied and the similarity function to be used for classification of the selected basic PD forms.
Go to article

Bibliography

1. Akbari A., Setayeshmehr A., Borsi H., Gockenbach E. (2010), Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers, IEEE Electrical Insulation Magazine, 26(6): 27–40, doi: 10.1109/MEI.2010.5599977.
2. Boczar T. (2001), Identification of a specific type of PD form acoustics emission frequency spectra, IEEE Transaction on Dielectric and Electrical Insulation, 8(4): 598–606, doi: 10.1109/94.946712.
3. Boczar T., Borucki S., Cichon A., Zmarzły D. (2009), Application Possibilities of Artificial Neural Networks for Recognizing Partial Discharges Measured by the Acoustic Emission Method, IEEE Transaction on Dielectric and Electrical Insulation, 16(1): 214–223, doi: 10.1109/TDEI.2009.4784570.
4. Boczar T., Cichon A., Borucki S. (2014), Diagnostic expert system of transformer insulation systems using the acoustic emission method, IEEE Transaction on Dielectric and Electrical Insulation, 21(2): 854–865, doi: 10.1109/TDEI.2013.004126.
5. Borucki S., Boczar T., Cichon A., Lorenc M. (2007), The evaluation of neural networks application for recognizing single-source PD forms generated in paper-oil insulation systems based on the AE signal analysis, European Physical Journal Special Topics, 154: 23–29, doi: 10.1140/epjst/e2008-00512-7.
6. Borucki S., Łuczak J. (2017), Assessment of the impact of an acoustic signal power spectral density frequency selection on partial discharges basic forms classification efficiency with the use of data clustering method [in Polish: Ocena wpływu doboru czestotliwosci widmowej gestosci mocy sygnału akustycznego na efektywnosc klasyfikacji podstawowych form wyładowan niezupełnych z uzyciem metody klasteryzacji], Energetyka, 7: 448–452.
7. Borucki S., Łuczak J., Zmarzły D. (2018), Using Clustering Methods for the Identification of Acoustic Emission Signals Generated by the Selected Form of Partial Discharge in Oil-Paper Insulation, Archives of Acoustics, 43(2): 207–215, doi: 10.24425/122368.
8. Castro Heredia L.C., Rodrigo Mor A. (2019), Density-based clustering methods for unsupervised separation of partial discharge sources, International Journal of Electrical Power & Energy Systems, 107: 224–230, doi: 10.1016/j.ijepes.2018.11.015.
9. Chia-Hung L., Chien-Hsien W., Ping-Zan H. (2009), Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers, Expert Systems with Applications, 36(2, part 1): 1371–1379, doi: 10.1016/j.eswa.2007.11.019.
10. Cichon A. (2013), Assessment of technical condition of on-load tap-changers by the method of acoustic emission, [in Polish: Ocena stanu technicznego podobciazeniowych przełaczników zaczepów metoda emisji akustycznej], Studia i Monografie, No. 352, Ofic. Wyd. Politechniki Opolskiej.
11. Fuhr J. (2005), Procedure for identification and localization of dangerous partial discharge sources in power transformers, IEEE Transaction on Dielectric and Electrical Insulation, 12(5): 1005–1014, doi: 10.1109/TDEI.2005.1522193.
12. Han J., Kamber M., Pei J. (2012), Data Mining. Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, Waltham.
13. Kapinos J., Glinka T., Drak B. (2014), Typical causes of operational failures in power transformers working in National Grid [in Polish: Typowe przyczyny uszkodzen eksploatacyjnych transformatorów energetycznych], Przeglad Elektrotechniczny, 90(1): 186–189, doi: 10.12915/pe.2014.01.45.
14. Kazmierski M., Olech W. (2013), Technical Diagnostics and Monitoring of Transformers [in Polish: Diagnostyka techniczna i monitoring transformatorów], Printing house of ZPBE Energopomiar-Elektryka Sp. z o.o., Gliwice.
15. Krzysko M., Wołynski W., Górecki T. Skorzybut M. (2008), Learning Systems – Pattern Recognition, Cluster Analysis and Dimensional Reduction [in Polish: Systemy uczace sie – rozpoznawanie wzorców, analiza skupien i redukcja wymiarowosci], Wydawnictwa Naukowo-Techniczne, Warszawa.
16. Kurtasz P. (2011), Application of a multi-comparative algorithm to classify acoustic emission signals generated by partial discharges [in Polish: Zastosowanie algorytmu multikomparacyjnego do klasyfikacji sygnałów emisji akustycznej generowanych przez wyładowania niezupełne], Ph.D. Dissertation, Opole University of Technology.
17. Lalitha E.M., Satish L. (2002),Wavelet analysis for classification of multi-source PD patterns, IEEE Transaction on Dielectric and Electrical Insulation, 7(1): 40– 47, doi: 10.1109/94.839339.
18. Ming-Shou S., Chung-Chu C., Chien-Yi C., Jiann-Fuh C. (2014), Classification of partial discharge events in GILBS using probabilistic neural networks and the fuzzy c-means clustering approach, International Journal of Electrical Power & Energy Systems, 61: 173–179, doi: 10.1016/j.ijepes.2014.03.054.
19. Mohan Rao U., Sood Y.R., Jarial R.K. (2015), Subtractive Clustering Fuzzy Expert System for Engineering Applications, Procedia Computer Science, 48: 77–83, doi: 10.1016/j.procs.2015.04.153.
20. Morzy T. (2013), Data mining. Methods and Algorithms [in Polish: Eksploracja danych. Metody i algorytmy], Wydawnictwo Naukowe PWN, Warszawa.
21. Olszewska A., Witos F. (2012), Location of partial discharge sources and analysis of signals in chosen power oil transformers by means of acoustic emission method, Acta Physica Polonica A, 122(5): 921–926.
22. Radionov A.A., Evdokimov S.A., Sarlybaev A.A., Karandaeva O.I. (2015), Application of Subtractive Clustering for Power Transformer Fault Diagnostics, Procedia Engineering, 129: 22–28, doi: 10.1016/j.proeng.2015.12.003.
23. Rodrigo Mor A., Castro Heredia L.C., Muñoz F.A. (2017), Effect of acquisition parameters on equivalent time and equivalent bandwidth algorithms for partial discharge clustering, International Journal of Electrical Power & Energy Systems, 88: 141–149, doi: 10.1016/j.ijepes.2016.12.017.
24. Rubio-Serrano J., Rojas-Moreno M., Posada J., Martienez-Tarifa J., Robles G., Garcia-Souto J. (2012), Electro-acoustic detection, identification and location of PD sources in oil-paper insulation systems, IEEE Transaction on Dielectric and Electrical Insulation, 19(5): 1569–1578, doi: 10.1109/TDEI. 2012.6311502.
25. Soltani A.A., Haghjoo F., Shahrtash S.M. (2012), Compensation of the effects of electrical sensors in measuring PD signals, IET Science, Measurement &Technology, 6(6): 494–501, doi: 10.1049/iet-smt.2012.0001.
Go to article

Authors and Affiliations

Sebastian Borucki
1
Jacek Łuczak
1
Marcin Lorenc
1

  1. Opole University of Technology, Opole, Poland
Download PDF Download RIS Download Bibtex

Abstract

A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.

Go to article

Authors and Affiliations

Yuancheng Li
Longqiang Ma
Download PDF Download RIS Download Bibtex

Abstract

The article presents the process of designing and manufacturing a prototype antenna based on the PIFA (Planar Inverted F Antenna) technology for the detection of UHF signals from partial discharges occurring in the power transformer insulation system. The main objective of the simulation studies was to obtain a frequency band covering the range of radio frequencies emitted by partial discharges in oil-paper insulation (surface discharges) and to adjust the dimensions of the antenna for its installation in the inspection window of the power transformer. The proposed structure consists of a radiating element in the shape of a rectangular meandering line and an additional parasitic element in the form of a specially selected resistor connecting the reflector with the radiator. The design of the prototype antenna was tested during laboratory tests in a high-voltage laboratory using a model of a transformer tank in which partial discharges were generated. The results of the measurements showed that the developed antenna has a higher sensitivity of partial discharge detection than other popular antennas used in transformer diagnostics, i.e. the disk antenna and the Hilbert fractal antenna. Due to high sensitivity, compact and simple structure and low production costs, the proposed PIFA antenna may be an interesting alternative to the currently used commercial antennas (mainly disk antennas) in on-line monitoring systems for partial discharges of power transformers.
Go to article

Authors and Affiliations

Cyprian Szymczak
1

  1. Poznan University of Technology, Institute of Electric Power Engineering, Piotrowo 3A, 60-965 Poznan

This page uses 'cookies'. Learn more