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Department of Electrical Machines,
Drives and Measurements

Ph.D. students of the Department with the main awards at the conference

Date: 24.11.2022

During the fifteenth edition of the Scientific Conference "Control in Power Electronics and Electric Drive SENE 2022," two Ph.D. students from the Department of Electrical Machines, Drives and Measurements, Michał Adamczyk, M.Sc. and Przemysław Pietrzak, M.Sc., won two main awards for the best papers by young scientists, in competitions organized by the Polish Section of the IEEE and the Łódź Branch of the Society of Polish Electrical Engineers (SEP).

Dyplom IEEE dla mgr. inż. Michała Adamczyka

The IEEE Poland Section award in the young scientist competition was awarded to Michał Adamczyk, M.Sc., for his work carried out under the supervision of Prof. Teresa Orłowska-Kowalska, M.Sc., entitled: Direct field-oriented induction motor fault-tolerant control of current sensors using a double modified Luenberger observer.

In our research, we focused on drivetrains with a higher degree of safety, such as electric vehicles. Our proposed solution allows stable operation of a vector-controlled drive, even after all sensors for stator current measurement are damaged. The introduced modification of the Luenberger observer, which performs both the role of detector and fault compensator, makes it possible to significantly increase the accuracy of stator current reproduction without additional cost and computational complexity – comments the laureate.

Dyplom SEP dla mgr. inż. Przemysława Pietrzaka

In the competition organized by the Łódź Branch of the SEP, the first degree prize was awarded to Mr. Przemysław Pietrzak, M.Sc., for the work carried out under the supervision of Mr. Marcin Wolkiewicz, Ph.D., D.Sc., entitled: Application of the short-time Fourier transform and artificial intelligence to the detection of defects in the stator windings of a permanent magnet synchronous motor.

When asked about the topic of the paper, the laureate replied: Our work deals with the diagnosis of winding short circuits in the stator windings of a PMSM motor, which are the most common electrical faults in this type of machine. Thanks to the use of short-time Fourier transform and artificial intelligence, we are able to detect the damage at an early stage with almost 100 percent accuracy, so that further degradation of the winding and complete destruction of the motor can be prevented, which reduces costs and is a greener solution than manufacturing and buying a new one.

Congratulations and we wish further success to our Ph.D. students!

[MA, PP – GT]

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