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    Tytuł:
    Innovative Extended Kalman Filter in a Coherent System for Detection and Compensation of Various Current Sensor Faults in the Induction Motor Drive
    Autorzy:
    Orlowska-Kowalska, Teresa
    Miniach, Magdalena
    Wydawca:
    IEEE
    Cytata wydawnicza:
    M. Miniach and T. Orlowska-Kowalska, "Innovative Extended Kalman Filter in a Coherent System for Detection and Compensation of Various Current Sensor Faults in the Induction Motor Drive," in IEEE Transactions on Industrial Electronics, vol. 72, no. 8, pp. 7772-7784, Aug. 2025, doi: 10.1109/TIE.2025.3528487.
    Opis:
    Measurement sensors, such as current sensors (CSs), are key components of any advanced AC motor drive. To ensure the highest quality and safety of the drive, CS faults, as well as significant measurement disturbances, must be detected and compensated for. This article presents the current sensor fault-tolerant control (CS-FTC) system using innovative extended Kalman filter (d-EKF) for induction motor (IM) drive. The parametric uncertainty correction factor estimated by d-EKF allows us to improve the quality of stator current estimation. The presented d-EKF not only performs CS fault compensation, but also provides reference signals to the residuum-based fault detector. The proposed CS-FTC enables the proper and stable speed regulation in the vector-controlled drive, also in a post-fault mode. An experimental validation of this solution, carried out over a wide range of speed and load changes in post-fault operation, confirmed the high quality of the stator current reconstruction and demonstrated the advantages and limitations of using the proposed d-EKF in the CS fault detection algorithm for the most frequently occurring types of CS faults.
    Narodowe Centrum Nauki, Polska, grant No. 2021/41/B/ST7/02971
    Paper NO. 24-TIE-3512, sent for review on August 27, 2024; accepted Decmber 28, 2024.
    Dostawca treści:
    Repozytorium Centrum Otwartej Nauki
    Artykuł
    Tytuł:
    Implementacja klasyfikatora uszkodzeń czujników prądu opartego na MLP w napędzie silnika indukcyjnego sterowanego wektorowo
    Implementation of MLP-based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive
    Autorzy:
    Teler, Krystian
    Orlowska-Kowalska, Teresa
    Orlowska-Kowalska Teresa
    Skowron, Maciej
    Współwytwórcy:
    Orlowska-Kowalska Teresa
    Wydawca:
    IEEE
    Cytata wydawnicza:
    K. Teler, M. Skowron and T. Orłowska-Kowalska, "Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive," in IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5702-5713, April 2024, doi: 10.1109/TII.2023.3336348.
    Opis:
    Narodowe Centrum Nauki, Polska, grant No. 2021/41/B/ST7/02971
    Paper NO. TII-23-3874, sent for review on 6 October 2023, accepted 12 November 2023.
    This article deals with the neural networks (NN)-based classification of current sensor (CS) faults in an induction motor drive system designed according to the idea of fault-tolerant control (FTC). The CS-FTC system ensures continued system functionality, even after a failure has occurred, not only through its ability to detect and isolate faults but also through its ability to compensate for them. The article focuses on the development of the technical implementation of a CS faults classifier based on NN in considering the problem. The proposed method uses the principle structure of a multilayer perceptron (MLP) based on single samples of the stator phase current signal. The tests demonstrate techniques that allow the development of NN input vectors while eliminating the constraints resulting from the machine's operating conditions (load change, rotational speed change). In addition, the possibility of optimizing the input vector of the network in terms of the analyzed damages is demonstrated by using harmonic analysis. The article shows the possibility of full use of the mathematical model in the practical implementation of diagnostic systems implemented on a real object. MLP aims to determine the CS fault category (no fault, gain change, offset, saturation, open circuit) and fault localization (phase A or B). The experimental verification on a laboratory stand with an induction motor of 1.1 kW confirmed the efficiency of the proposed method in 97.2% of the cases.
    Dostawca treści:
    Repozytorium Centrum Otwartej Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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