05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access 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, MartinAlthough 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.Item Open Access Assessment of UHF frequency range for failure classification in power transformers(2024) Schiewaldt, Karl; de Castro, Bruno Albuquerque; Ardila-Rey, Jorge Alfredo; Franchin, Marcelo Nicoletti; Andreoli, André Luiz; Tenbohlen, StefanUltrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.Item Open Access Transformer winding fault classification and condition assessment based on random forest using FRA(2023) Tahir, Mehran; Tenbohlen, StefanAt 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.Item Open Access Load-flow-based calculation of initial short-circuit currents for converter-based power system(2025) Deepak, Deepak; Oetoyo, Anisatur Rizqi; Rudion, Krzysztof; John, Christoph; Abele, HansShort-circuit current is a key characteristic value for synchronous generator-based power systems. It is employed for different applications during the planning and operation phases. The proportion of converter-interfaced units is increasing in order to integrate more renewable energy sources into the system. These units have different fault current characteristics due to their physical properties and operation strategies. Consequently, the network’s short-circuit current profile is changing, both in terms of magnitude and injection time. Therefore, accurately estimating fault currents is crucial for reliable power system planning and operation. Traditionally, two calculation methods are employed: the equivalent voltage source (IEC 60909/VDE 0102) and the superimposition (complete) method. In this work, the assumptions, simplifications, and limitations from both types of methods are addressed. As a result, a new load-flow-based method is presented, improving the static modeling of generating units and the accuracy in the estimation of short-circuit currents. The method is tested for mixed generation types comprising of synchronous generators, and grid-following (current source) and grid-forming (voltage source before and current source after the current limit) converters. All methods are compared against detailed time-domain RMS simulations using a modified IEEE-39 bus system and a real network from ENTSO-E. It is shown that the proposed method provides the best accuracy in the calculation of initial short-circuit currents for converter-based power systems.