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%0 Thesis
%A Quabeck, Stefan
%T Modeling of parasitic currents and fault detection in electrical traction drives
%V 170
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2023-10920
%B Aachener Beiträge des ISEA
%P 1 Online-Ressource : Illustrationen
%D 2023
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
%X The trend towards increasing integration of electric machine, inverter, and gearbox into ane-axle yields significant advantages in terms of power density and ease-of-use. Moreover,with the reduced size and increased temperature resilience of wide-bandgap semiconductors,the inverter can be integrated closer to the windings of the machine. This reducescable length and increases the power density even further. However, this close integrationalso poses several new challenges that must be overcome. Parasitic capacitive andinductive coupling between the windings and other parts of the drive increases due to the close proximity. The combination of steep voltage slopes from the wide-bandgap devicesand increased parasitic capacitances causes high parasitic currents to flow through thedrive. These parasitic currents can significantly reduce the lifetime of an electrical drive. Theyflow through the bearings and any components connected to the shaft, can cause severedamage to the bearings and gearbox, and deteriorate the insulation and the lubricantsused in the machine. To understand the physical causes and to estimate parasitic currentsinside electric machines, this work, therefore, presents a high-frequency model of anelectrical machine. An automated parameter fine-tuning procedure helps to increase themodel accuracy and reduces parameterization effort. The model is experimentally verifiedwith a minimally-invasive bearing current measurement method. The model successfullypredicts the amplitude of bearing voltages and is used to evaluate countermeasures, suchas a shaft grounding brush. The effectiveness of this shaft grounding brush is confirmedwith measurements. However, faults are still bound to occur in the machine even when parasitic bearing currentsare mitigated. For reliable electrical powertrains, fault detection is therefore essential. For this purpose, the frequency spectrum of the phase current can be analyzed, sinceit contains much more information than only the torque and speed of the machine. Manyfaults leave detectable signatures in the spectrum. The accuracy of traditional spectrumbasedfault detection methods, however, severely depends on the operating point. This work proposes a combination of spectrum-based fault detection methods, informationknown from the controller, and machine learning. This combination leads to high faultdetection and classification accuracy over the whole operating range. Even incipient faultsare reliably detected. Faults also influence the parasitic behavior of electric machines. This interdependence isinvestigated in detail and the occurrence of full and partial bearing voltage breakdowns is analyzed. Thus, this thesis delivers an in-depth understanding of the parasitic effectsin electric machines and how they are influenced by machine faults. While bearing faultscause more bearing voltage breakdowns to occur, for broken rotor bars, no such influenceis observed.
%F PUB:(DE-HGF)11 ; PUB:(DE-HGF)3
%9 Dissertation / PhD ThesisBook
%R 10.18154/RWTH-2023-10920
%U https://publications.rwth-aachen.de/record/973652