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@PHDTHESIS{Quabeck:973652,
      author       = {Quabeck, Stefan},
      othercontributors = {de Doncker, Rik W. and Mütze, Annette},
      title        = {{M}odeling of parasitic currents and fault detection in
                      electrical traction drives},
      volume       = {170},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Institute for Power Electronics and Electrical Drives
                      (ISEA)},
      reportid     = {RWTH-2023-10920},
      series       = {Aachener Beiträge des ISEA},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2023},
      abstract     = {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.},
      cin          = {614510 / 614500},
      ddc          = {621.3},
      cid          = {$I:(DE-82)614510_20140620$ / $I:(DE-82)614500_20201203$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2023-10920},
      url          = {https://publications.rwth-aachen.de/record/973652},
}