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Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter

Author

Listed:
  • Waseem El Sayed

    (Electrical and control Department, College of Engineering and Technology, Arab Academy For Science and Technology and Maritime Transport, Abou Keer Campus, P.O. Box 1029, Alexandria 21500, Egypt
    Institute of Automatics, Electronics and Electrical Engineering, University of Zielona Gora, 65-417 Zielona Gora, Poland)

  • Mostafa Abd El Geliel

    (Electrical and control Department, College of Engineering and Technology, Arab Academy For Science and Technology and Maritime Transport, Abou Keer Campus, P.O. Box 1029, Alexandria 21500, Egypt)

  • Ahmed Lotfy

    (Electrical and control Department, College of Engineering and Technology, Arab Academy For Science and Technology and Maritime Transport, Abou Keer Campus, P.O. Box 1029, Alexandria 21500, Egypt)

Abstract

Since the permeant magnet synchronous generator (PMSG) has many applications in particular safety-critical applications, enhancing PMSG availability has become essential. An effective tool for enhancing PMSG availability and reliability is continuous monitoring and diagnosis of the machine. Therefore, designing a robust fault diagnosis (FD) and fault tolerant system (FTS) of PMSG is essential for such applications. This paper describes an FD method that monitors online stator winding partial inter-turn faults in PMSGs. The fault appears in the direct and quadrature (dq)-frame equations of the machine. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) were used to detect the percentage and the place of the fault. The proposed techniques have been simulated for different fault scenarios using Matlab ® /Simulink ® . The results of the EKF estimation responses simulation were validated with the practical implementation results of tests that were performed with a prototype PMSG used in the Arab Academy For Science and Technology (AAST) machine lab. The results showed impressive responses with different operating conditions when exposed to different fault states to prevent the development of complete failure.

Suggested Citation

  • Waseem El Sayed & Mostafa Abd El Geliel & Ahmed Lotfy, 2020. "Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter," Energies, MDPI, vol. 13(11), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2972-:d:369406
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    References listed on IDEAS

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    1. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    2. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
    3. Oscar Duque-Perez & Carlos Del Pozo-Gallego & Daniel Morinigo-Sotelo & Wagner Fontes Godoy, 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods," Energies, MDPI, vol. 12(17), pages 1-13, September.
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    Cited by:

    1. Rodolfo V. Rocha & Renato M. Monaro, 2023. "Algorithm for Fast Detection of Stator Turn Faultsin Variable-Speed Synchronous Generators," Energies, MDPI, vol. 16(5), pages 1-23, March.
    2. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.
    3. Mojtaba Nasiri & Saleh Mobayen & Behdad Faridpak & Afef Fekih & Arthur Chang, 2020. "Small-Signal Modeling of PMSG-Based Wind Turbine for Low Voltage Ride-Through and Artificial Intelligent Studies," Energies, MDPI, vol. 13(24), pages 1-18, December.
    4. Apostolos Lamprokostopoulos & Epameinondas Mitronikas & Alexandra Barmpatza, 2022. "Detection of Demagnetization Faults in Axial Flux Permanent-Magnet Synchronous Wind Generators," Energies, MDPI, vol. 15(9), pages 1-15, April.
    5. Afef Fekih & Hamed Habibi & Silvio Simani, 2022. "Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview," Energies, MDPI, vol. 15(19), pages 1-21, September.

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