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Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines

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  • Cho, Seongpil
  • Gao, Zhen
  • Moan, Torgeir

Abstract

This paper presents model-based fault detection, fault isolation, and fault-tolerant control schemes focused on blade pitch systems in floating wind turbines. Fault detection, isolation, and accommodation techniques are required to achieve high power capture efficiency and structural reliability in floating wind turbines. Faults in blade pitch systems should be detected at an early stage to prevent catastrophic failures. To detect faults of the blade pitch systems, a Kalman filter is designed to estimate the blade pitch angle of the system. The fault isolation algorithm is based on inference methods and capable of determining the fault type, location, magnitude and time. The fault-tolerant controller based on a reconfiguration block with a virtual sensor and shutdown mode controls the floating wind turbine to avoid unexpected external loads. The proposed methods are demonstrated in case studies with stochastic wind and wave conditions that considering different types of faults, such as biases and fixed outputs in pitch sensors and stuck pitch actuators. The simulation results show that the proposed methods can detect and isolate multiple faults effectively at an early stage. Additionally, the effectiveness of the fault-tolerant control systems for different load cases for single and multiple fault conditions is verified by numerical simulations.

Suggested Citation

  • Cho, Seongpil & Gao, Zhen & Moan, Torgeir, 2018. "Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 306-321.
    • Handle: RePEc:eee:renene:v:120:y:2018:i:c:p:306-321
    DOI: 10.1016/j.renene.2017.12.102
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    References listed on IDEAS

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    2. Yolanda Vidal & Christian Tutivén & José Rodellar & Leonardo Acho, 2015. "Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator," Energies, MDPI, vol. 8(5), pages 1-17, May.
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    Cited by:

    1. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    2. Noor Hussain & Mashood Nasir & Juan Carlos Vasquez & Josep M. Guerrero, 2020. "Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review," Energies, MDPI, vol. 13(9), pages 1-31, May.
    3. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    4. Liu, Yichao & Ferrari, Riccardo & Wu, Ping & Jiang, Xiaoli & Li, Sunwei & Wingerden, Jan-Willem van, 2021. "Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach," Renewable Energy, Elsevier, vol. 164(C), pages 391-406.
    5. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    6. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    7. Guodong You & Tao Xu & Honglin Su & Xiaoxin Hou & Jisheng Li, 2019. "Fault-Tolerant Control for Actuator Faults of Wind Energy Conversion System," Energies, MDPI, vol. 12(12), pages 1-16, June.
    8. Sofiane Bououden & Fouad Allouani & Abdelaziz Abboudi & Mohammed Chadli & Ilyes Boulkaibet & Zaher Al Barakeh & Bilel Neji & Raymond Ghandour, 2023. "Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults," Energies, MDPI, vol. 16(2), pages 1-21, January.
    9. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.
    10. Ke Luo & Yingying Jiao, 2021. "Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-24, March.
    11. Do, M. Hung & Söffker, Dirk, 2021. "State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Truong, Hoai Vu Anh & Dang, Tri Dung & Vo, Cong Phat & Ahn, Kyoung Kwan, 2022. "Active control strategies for system enhancement and load mitigation of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    13. Dibaj, Ali & Gao, Zhen & Nejad, Amir R., 2023. "Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements," Renewable Energy, Elsevier, vol. 203(C), pages 161-176.
    14. Mazare, Mahmood & Taghizadeh, Mostafa & Ghaf-Ghanbari, Pegah, 2021. "Fault tolerant control of wind turbines with simultaneous actuator and sensor faults using adaptive time delay control," Renewable Energy, Elsevier, vol. 174(C), pages 86-101.
    15. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    16. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    17. Palanimuthu, Kumarasamy & Joo, Young Hoon, 2023. "Reliability improvement of the large-scale wind turbines with actuator faults using a robust fault-tolerant synergetic pitch control," Renewable Energy, Elsevier, vol. 217(C).
    18. Cong Yang & Zheng Qian & Yan Pei & Lu Wei, 2018. "A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems," Energies, MDPI, vol. 11(8), pages 1-17, August.
    19. Xiaoyi Qian & Yuxian Zhang & Mohammed Gendeel, 2019. "State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines," Energies, MDPI, vol. 12(11), pages 1-18, May.
    20. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    21. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    22. Mazare, Mahmood & Taghizadeh, Mostafa, 2022. "Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines," Renewable Energy, Elsevier, vol. 188(C), pages 545-560.
    23. Li, Jianshen & Wang, Shuangxin, 2021. "Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults," Renewable Energy, Elsevier, vol. 174(C), pages 293-304.

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