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Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

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

Abstract

This paper describes the development of a fault detection and diagnosis method to automatically identify different fault conditions of a hydraulic blade pitch system in a spar-type floating wind turbine. For fault detection, a Kalman filter is employed to estimate the blade pitch angle and valve spool position of the blade pitch system. The fault diagnosis scheme is based on an artificial neural network method with supervised learning that is capable of diagnosing a predetermined fault type. The neural network algorithm produces a predictive model with training, validation and test procedures after the final performance evaluation. The validation and test procedures of the artificial neural network model are conducted with the training model to prove the model performance. The proposed method is demonstrated in case studies of a spar floating wind turbine with stochastic wind and wave conditions and with consideration of six different types of faults, such as biases and fixed outputs in pitch sensors and excessive friction, slit-lock, wrong voltage, and circuit shortage in actuators. The fault diagnosis results from the final performance evaluation show that the proposed methods work effectively with good performance.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:169:y:2021:i:c:p:1-13
    DOI: 10.1016/j.renene.2020.12.116
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    References listed on IDEAS

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    1. Yingning Qiu & Lang Chen & Yanhui Feng & Yili Xu, 2017. "An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 10(8), pages 1-21, July.
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    3. 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.
    4. 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.
    5. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
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    Cited by:

    1. Ghigo, Alberto & Faraggiana, Emilio & Giorgi, Giuseppe & Mattiazzo, Giuliana & Bracco, Giovanni, 2024. "Floating Vertical Axis Wind Turbines for offshore applications among potentialities and challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
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    4. 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.
    5. 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.
    6. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    7. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    8. 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.
    9. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    10. Mehlan, Felix C. & Nejad, Amir R., 2023. "Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring," Renewable Energy, Elsevier, vol. 212(C), pages 70-81.
    11. Kenny-Jesús Flores-Huamán & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Pablo Cortés, 2024. "Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach," Mathematics, MDPI, vol. 12(15), pages 1-34, July.
    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. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    14. 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.
    15. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    16. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
    17. Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
    18. Rahimilarki, Reihane & Gao, Zhiwei & Jin, Nanlin & Zhang, Aihua, 2022. "Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine," Renewable Energy, Elsevier, vol. 185(C), pages 916-931.
    19. 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.
    20. 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.

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