Detection, identification and localization of partial discharges in power transformers using UHF techniques

Download files
Access & Terms of Use
open access
Copyright: Sinaga, Herman Halomoan
Altmetric
Abstract
Partial discharge (PD) detection using the ultra high frequency (UHF) method has proven viable in monitoring the insulation condition of GIS. Recently, it is being extended and applied to transformer diagnostics. The UHF PD detection method shows advantages over traditional electrical PD detection such as the standardized IEC 60270 method. The main advantage of the UHF method is its impunity over environmental noise. The UHF detection method applies sensors (antennas) to detect the electromagnetic signals emitted by the PD source. These signals, once picked up by the sensor, can then be captured with appropriate recording equipment. The sensor is thus one of the most important parts of UHF PD detection. The sensors must be able to pick up the electromagnetic signals which lie in the UHF range. In this thesis, the sensors were designed using special purpose electromagnetic software. Four types of antenna were designed with various dimensional constraints: monopole, conical-skirt monopole, spiral and log-spiral. All sensors were then tested to find the most suitable sensor for PD detection and localization. The log-spiral sensor was found to be a better sensor for PD detection and recognition whilst the monopole sensor was more suited to PD localization. PD detection and recognition were carried out by recording the PD signals in time and frequency domain. The recorded signals were then used as input to recognize the different PD defect types. Recognition was achieved by applying neuro-fuzzy and artificial neural network methods. The results show that both methods can be used to recognize and classify the PD sources with high accuracy. An array of 4 sensors was used for PD localization. The PD location can be determined from the time difference of arrival (TDOA) of the signals arriving at sensors and at different positions. Three methods were used to determine the TDOA, i.e. first peaks, cross-correlation and cumulative energy curve. The first-peaks method showed the lowest error compared to the other two methods, followed thereafter by the cross-correlation and the cumulative energy curve method.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Author(s)
Sinaga, Herman Halomoan
Supervisor(s)
Phung, Toan
Blackburn, Trevor
Creator(s)
Editor(s)
Translator(s)
Curator(s)
Designer(s)
Arranger(s)
Composer(s)
Recordist(s)
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
2012
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
Files
download whole.pdf 4.53 MB Adobe Portable Document Format
Related dataset(s)