Email: teresa.orlowska-kowalska@pwr.edu.pl

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

ul. Smoluchowskiego 19, 50-372 Wrocław
building A-10, room 320 (3rd floor)
phone  +48 71 320 2640

secretariat: phone +48 71 320 3467

Zespół Napędu Elektrycznego, Energoelektroniki i Elektromobilności

Electric Drive, Power Electronics and Electromobility Team

(Information about the Team, Offer for Industry, Scientific Research, Teaching)

Office hours

• according to the information at:
• http://weny.pwr.edu.pl/studenci/konsultacje/pracownikow-katedry-w5-k3

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Research fields

• Controlled electrical drives, artificial intelligence; especially:
  • state variable estimation for sensorless AC drives – sensorless control,
  • vector control methods for AC motors (induction motors and PMSM),
  • variable-Structure-Control theory application for state variables control and estimation in electrical drives,
  • neural networks application for state variables control and estimation in electrical drives, including drives with multiple elastic couplings,
  • methods for fault detection, localization and compensation in converter-fed drive systems (Fault Tolerant Control).

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Recent papers

2025

• Miniach M, Orlowska-Kowalska T., Innovative Extended Kalman Filter in a Coherent System for Detection and Compensation of Various Current Sensor Faults in the Induction Motor Drive. IEEE Trans. on Industrial Electronics, 2025, early access
• Benlaloui I., Fezzani A., Orlowska-Kowalska T., Chrifi-Alaoui L., Drid S., Online Short-Circuit Fault Detector of the Induction Motor Winding based on MRAS Technique. IEEE Trans. on Industrial Electronics, 2025, early access

2024

• Teler K., Skowron M., Orlowska-Kowalska T., Implementation of MLP-based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive. IEEE Trans. on Industrial Informatics, vol. 20, no. 4, pp. 5702-5713, April 2024.
• Tir Z., Orlowska-Kowalska T., Ahmed H., Houari A., Adaptive High Gain Observer Based MRAS for Sensorless Induction Motor Drives. IEEE Trans. on Industrial Electronics, vol. 71, no.1 , 2024, pp. 271-281
• Teler K., Orlowska-Kowalska T., Skowron M., Verification of the MLP network-based current sensor fault classifier for various vector-controlled AC motor drives. Bulletin of the Polish Academy of Sciences, vol. 72(6), 2024, art. e150336, pp. 1-15.
• Adamczyk M., Orlowska-Kowalska T., Current sensor fault-tolerant control based on modified Luenberger observers for safety-critical vector-controlled induction motor drive. Bulletin of the Polish Academy of Sciences, vol. 72(65), 2024, art. e151041, pp. 1-15.
• Orlowska-Kowalska T., Miniach M., Adamczyk M., Compensation of current sensor faults in induction motor drive using modified extended Kalman files. Electronics, 2024, 13(3), 641

2023

• Pietrzak P., WolkiewiczM., Orlowska-Kowalska T., PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network. IEEE Trans. Industrial Electronics, 70, no. 5, May. 2023 pp. 5192-5202
• Skowron M., Orlowska-Kowalska T., Kowalski C.T., Diagnosis of Stator Winding and Permanent Magnet Faults of PMSM Drive Using Shallow Neural Networks. Electronics 2023, 12, 1068, pp.1-15
• Teler K., Orłowska-Kowalska T., Analysis of the stator current prediction capabilities in induction motor drive using the LSTM network. Power Electronics and Drives. 2023, vol. 8, no. 1(43), s. 31-52
• Adamczyk M., Orlowska-Kowalska T., Active current sensor fault-tolerant control of induction motor drive with online neural network-based rotor and stator resistance estimation, Power Electronics and Drives. 2023, vol. 8, no. 1(43), s. 235-251
• Adamczyk M., Orlowska-Kowalska T., Analysis of the influence of voltage source inverter dead time and its compensation on the quality of stator current estimation in induction motor drive, Przegląd Elektrotechniczny, 99, no. 4, 2023, pp.1-5.
• Adamczyk M., Orlowska-Kowalska T., Direct field-oriented current sensor fault tolerant control of induction motor with dual modified Luenberger observer. Przegląd Elektrotechniczny, vol. 99, no. 4, 2023, pp.43-50

2022

• Adamczyk M., Orlowska-Kowalska T., Post-Fault Direct Field Oriented Control of Induction Motor Drive using Adaptive Virtual Current Sensor. IEEE Trans. Industrial Electronics, 69, 2022, no. 4, pp. 3418 - 3427
• Skowron M., Orłowska-Kowalska T., Kowalski C.T., Detection of permanent magnet damage of PMSM drive based on direct analysis of the stator phase currents using convolutional neural network, IEEE Trans. on Industrial Electronics, vol. 69, no. 12, s. 13665-13675, 2022
• Skowron M., Krzysztofiak M., Orłowska-Kowalska T., Effectiveness of neural fault detectors of permanent magnet synchronous motor trained with symptoms from field-circuit modeling, IEEE Access, vol. 10, 2022, pp. 104598-104611
• Orlowska-Kowalska T., Wolkiewicz M., Pietrzak p., Skowron M., Ewert P., Tarchala G., Krzysztofiak M., Kowalski C.T. Fault diagnosis and fault -tolerant control of PMSM drives – state of the art and future challenges. IEEE Access, vol. 10, 2022, pp. 59979–60024
• Adamczyk M., Orlowska-Kowalska T., 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, vol.70, no.1, art. no. e140099, 2022, pp. 1-11
• Skowron M., Teler K., Adamczyk M., Orlowska-Kowalska T., Classification of single current sensor faults in fault-tolerant induction motor drive using neural network approach. Energies 2022, vol. 15, (18), 6646
• Skowron M., Kowalski C.T., Orlowska-Kowalska T., Impact of the convolutional neural network structure and training parameters on the effectiveness of the diagnostic systems of modern AC motor drives. Energies 2022, vol. 15, (19), 7008
• Adamczyk M., Orlowska-Kowalska T., Influence of parameter uncertainty to stator current reconstruction using Modified Luenberger Observer for current sensor Fault-Tolerant Induction Motor Drive. Sensors, 2022, 22, 9813

2021

• Skowron M., Orłowska-Kowalska T., Kowalski C.T., Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data. IET Electric Power Applications. 2021, s. 1-15 doi: 10.1049/elp2.12066
• Krzysztofiak M., Skowron M., Orlowska-Kowalska T., Analysis of the impact of stator inter-turn short circuits on PMSM drive with scalar and vector control, Energies. 2021, 14, no. 1, art. 153, 1-20 doi: 10.3390/en14010153
• Ewert P., Orlowska-Kowalska T., Jankowska K., Effectiveness analysis of PMSM motor rolling bearing fault detectors based on vibration analysis and shallow neural networks. Energies. 2021, vol. 14, no. 3, art. 712, 1-24 doi: 10.3390/en14030712

2020

• Orlowska-Kowalska T., Korzonek M., Tarchala G., Performance analysis of speed-sesorless induction motor drive using discrete current-error based MRAS estimators, Energies, 2020, 13, 2595,1-23 doi: 10.3390/en13102595
• Korzonek M., Tarchala G., Orlowska-Kowalska T., Simple Stability Enhancement Method for Stator Current Error-based Speed Estimator MRAS^CC for Induction Motor Drive, IEEE Trans. Industrial Electronics, 2020, 67, no.7, pp,5854-5866; doi: 10.1109/TIE.2019.2960726
• Tarchała G., Orlowska-Kowalska T., Discrete sliding-mode speed control of induction motor using time-varying switching line, Electronics 2020, 9, 185, pp.1-18, doi:10.3390/electronics9010185
• Skowron M., Orlowska-Kowalska T., Wolkiewicz M., Kowalski C.T., Convolutional Neural Network Based Incipient Stator Fault Detection of Inverter-Fed Induction Motor Using Stator Current Measurement Data, Energies 2020, 13, 1475; doi:10.3390/en13061475
• Ewert P, Kowalski C.T., Orlowska-Kowalska T., Low-cost monitoring and diagnosis system for rolling bearing faults of the induction motor based on neural network approach; Electronics 2020, 9(9), 1334; doi: 3390/electronics9091334
• Skowron M., Orłowska-Kowalska T., Efficiency of cascade-connected neural networks in detecting initial faults to induction motor drive electric windings; Electronics 2020, 9(8), 1314 doi:10.3390/electronics9081314 

2019

• Orlowska-Kowalska T., Korzonek M., Tarchala G., Stability Improvement Methods of the Adaptive Full-Order Observer for Sensorless Induction Motor Drive – Comparative Study, IEEE Trans. Industrial Informatics, 2019, vol. 15, no. 11, pp. 6144-6126
• Korzonek M., Tarchała G., Orłowska-Kowalska T., A review on MRAS-type speed estimators for reliable and efficient induction motor drives, ISA Transactions 2019, 93, no. 10, pp. 1–13
• Adamczyk M., Orłowska-Kowalska T., Virtual Current Sensor in the Fault-Tolerant Field-Oriented Control Structure of an Induction Motor Drive, Sensors, 2019, vol. 19, 22, 4979, pp. 1-15
• Skowron M., Wolkiewicz M., Kowalski C.T. Orlowska-KowalskaT., Effectiveness of selected neural network structures based on axial flux analysis in stator and rotor winding incipient fault detection of inverter-fed induction motors, Energies, 2019, vol. 12, no. 12, art.2392, s. 1-20
• Skowron, Wolkiewicz M., Orlowska-Kowalska T., Kowalski C. Application of self-organizing neural networks to electrical fault classification in induction motors. Applied Sciences 2019, vol. 9, no. 4, art. 616, s. 1-22.

2018

• Tarchala G., Orlowska-Kowalska T., Equivalent-Signal Based Sliding Mode Speed MRAS-type Estimator for Induction Motor Drive, IEEE Trans. Industrial Electronics, 2018, 65, no 9, pp. 6936 - 6947. 2017

2017

• Orlowska-Kowalska T., Korzonek M., Tarchala G., Stability Analysis of Selected Speed Estimators for Induction Motor Drive in Regenerating Mode - a Comparative Study, IEEE Trans. Industrial Electronics, 2017, 64, no.10, 7721-7730.
• Sobański P., Orłowska-Kowalska T., Faults diagnosis and control in a low-cost fault-tolerant induction motor drive system, Mathematics and Computers in Simulation, vol. 131, January 2017, pp. 217-233.

2016

• Wolkiewicz M, Tarchala G., Orlowska-Kowalska T., Kowalski C.T., On-line stator inter-turn short circuits monitoring in the DFOC induction motor .drive, IEEE Trans. Industrial Electronics, 2016, vol. 63, no.4, 2517-2528.

Papers in DONA database