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Condition Monitoring Using Digital Fault-Detection Approach for Pitch System in Wind Turbines

Author

Listed:
  • Abdelmoumen Saci

    (Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000, Algeria)

  • Mohamed Nadour

    (Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000, Algeria)

  • Lakhmissi Cherroun

    (Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000, Algeria)

  • Ahmed Hafaifa

    (Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000, Algeria)

  • Abdellah Kouzou

    (Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, Djelfa 17000, Algeria
    Institute of High-Power Converter Systems (HLU), Technical University of Munich (TUM), 80333 Munich, Germany)

  • Jose Rodriguez

    (Center for Energy Transition, Universidad San Sebastián, Santiago 8420524, Chile)

  • Mohamed Abdelrahem

    (Institute of High-Power Converter Systems (HLU), Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

Abstract

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults’ occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability.

Suggested Citation

  • Abdelmoumen Saci & Mohamed Nadour & Lakhmissi Cherroun & Ahmed Hafaifa & Abdellah Kouzou & Jose Rodriguez & Mohamed Abdelrahem, 2024. "Condition Monitoring Using Digital Fault-Detection Approach for Pitch System in Wind Turbines," Energies, MDPI, vol. 17(16), pages 1-35, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4016-:d:1455543
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    References listed on IDEAS

    as
    1. Francesco Mazzeo & Derek Micheletto & Alessandro Talamelli & Antonio Segalini, 2022. "An Experimental Study on a Wind Turbine Rotor Affected by Pitch Imbalance," Energies, MDPI, vol. 15(22), pages 1-16, November.
    2. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.
    3. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    4. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
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