05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Publication Type: search.filters.UBSTyp.ZeitschriftenartikelAuthor: search.filters.author.Tenbohlen, StefanInstitute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungSubject: search.filters.subject.621.3

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    Quantitative analysis of the sensitivity of UHF sensor positions on a 420 kV power transformer based on electromagnetic simulation
    (2019) Beura, Chandra Prakash; Beltle, Michael; Tenbohlen, Stefan; Siegel, Martin
    With an increasing interest in ultra-high frequency (UHF) partial discharge (PD) measurements for the continuous monitoring of power transformers, it is necessary to know where to place the UHF sensors on the tank wall. Placing a sensor in an area with many obstructions may lead to a decrease in sensitivity to the UHF signals. In this contribution, a previously validated simulation model of a three-phase 300 MVA, 420 kV power transformer is used to perform a sensitivity analysis to determine the most sensitive sensor positions on the tank wall when PD activity occurs inside the windings. A matrix of UHF sensors located on the transformer tank is used to perform the sensitivity analysis. Some of the windings are designed as layer windings, thus preventing the UHF signals from traveling through them and creating a realistic situation with very indirect propagation from source to sensor. Based on these findings, sensor configurations optimized for UHF signal sensitivity, which is also required for PD source localization, are recommended for localization purposes. Additionally, the propagation and attenuation of the UHF signals inside the windings and the tank are discussed in both oil and air.
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    Frequency range of UHF PD measurements in power transformers
    (2023) Tenbohlen, Stefan; Beura, Chandra Prakash; Sikorski, Wojciech; Albarracín Sánchez, Ricardo; Albuquerque de Castro, Bruno; Beltle, Michael; Fehlmann, Pascal; Judd, Martin; Werner, Falk; Siegel, Martin
    Although partial discharge (PD) measurement is a well-accepted technology to assess the quality of the insulation system of power transformers, there are still uncertainties about which frequency range PDs radiate and which frequency range should be evaluated in a measurement. This paper discusses both a UHF PD frequency range obtained from studies investigating laboratory experiments and a frequency range from numerous practical use cases with online and on-site measurements. The literature review reveals a frequency spectrum of ultrahigh-frequency (UHF) PD measurements in the range of 200 MHz to 1 GHz for most publications. Newer publications extend this range from 3 to 6 GHz. The use cases present UHF PD measurements at transformers with power ratings up to 1000 MVA to determine frequency ranges which are considered effective for practical applications. The “common” frequency range, where measurements from all use cases provide signal power, is from approximately 400 MHz to 900 MHz, but it is noted that the individual frequency range, as well as the peak UHF signal power, strongly varies from case to case. We conclude from the discussed laboratory experiments and practical observations that UHF PD measurements in power transformers using either valve or window antennas, according to Cigré, are feasible methods to detect PD.
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    Transformer winding fault classification and condition assessment based on random forest using FRA
    (2023) Tahir, Mehran; Tenbohlen, Stefan
    At present, the condition assessment of transformer winding based on frequency response analysis (FRA) measurements demands skilled personnel. Despite many research efforts in the last decade, there is still no definitive methodology for the interpretation and condition assessment of transformer winding based on FRA results, and this is a major challenge for the industrial application of the FRA method. To overcome this challenge, this paper proposes a transformer condition assessment (TCA) algorithm, which is based on numerical indices, and a supervised machine learning technique to develop a method for the automatic interpretation of FRA results. For this purpose, random forest (RF) classifiers were developed for the first time to identify the condition of transformer winding and classify different faults in the transformer windings. Mainly, six common states of the transformer were classified in this research, i.e., healthy transformer, healthy transformer with saturated core, mechanically damaged winding, short-circuited winding, open-circuited winding, and repeatability issues. In this research, the data from 139 FRA measurements performed in more than 80 power transformers were used. The database belongs to the transformers having different ratings, sizes, designs, and manufacturers. The results reveal that the proposed TCA algorithm can effectively assess the transformer winding condition with up to 93% accuracy without much human intervention.