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Faculty of Electrical Engineering

Michał Adamczyk, PhD

Email: michal.adamczyk@pwr.edu.pl

Unit: Faculty of Electrical Engineering » Department of Electrical Machines, Drives and Measurements

ul. M. Smoluchowskiego 19, 50-372 Wrocław joanna_budzisz.jpg
Building A-5, room 206, I floor
Tel: 71 320 37 16

Office hours:

  • The current list of office hours is available here.

Research fields:

  • Advanced techniques of electric drives control
  • Current sensors fault-tolerant control
  • Estimators of state variables
  • Estimators of motors parameters
  • Luenberger Observers
  • Current sensor fault detectors and classifiers

Research project:

  • Participation in the project obtained in the OPUS 21 competition under the direction of prof. dr hab. inż. Teresa Orłowska-Kowalska, Methods of current sensors faults detection and compensation for induction and synchronous motor drives based on modified state variable observers.

Recent papers:

  • Michał Adamczyk, Teresa Orłowska-Kowalska, Virtual current sensor in the fault-tolerant field-oriented control structure of an induction motor drive. Sensors. 2019, vol. 19, nr 22, art. 4979, s. 1-15.
  • Michał Adamczyk, Teresa Orłowska-Kowalska, Self-correcting virtual current sensor based on the modified Luenberger observer for fault-tolerant induction motor drive. Energies. 2021, vol. 14, nr 20, art. 6767, s. 1-16.
  • Michał Adamczyk, Teresa Orłowska-Kowalska, Influence of the stator current reconstruction method on direct torque control of induction motor drive in current sensor postfault operation. Bulletin of the Polish Academy of Sciences. Technical Sciences. 2022, vol. 70, nr 1, art. e140099, s. 1-11.
  • Michał Adamczyk, Teresa Orłowska-Kowalska, Postfault direct field-oriented control of induction motor drive using Adaptive Virtual Current Sensor. IEEE Transactions on Industrial Electronics. 2022, vol. 69, nr 4, s. 3418-3427.
  • Maciej Skowron, Krystian Teler, Michał Adamczyk, Teresa Orłowska-Kowalska, Classification of single current sensor failures in fault-tolerant induction motor drive using neural network. Energies. 2022, vol. 15, nr 18, art. 6646, s. 1-18.

Full list of publications.

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