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Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system

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  • Korkos, Panagiotis
  • Linjama, Matti
  • Kleemola, Jaakko
  • Lehtovaara, Arto

Abstract

The performance of wind turbines can be improved by processing supervisory control and data acquisition (SCADA) data. SCADA data can be processed in a reasonable time to enhance decisions made about maintenance schedules. The pitch system is critical in improving wind turbine operation by analysing data of the most relevant SCADA features. This study gathers the most significant pitch faults, and by implementing the adaptive neuro fuzzy inference system (ANFIS) technique it demonstrates the fault detection potential of this technique. The proposed approach includes the detailed pre-processing of SCADA data, emphasising the labelling process, in which a modified power curve monitoring method is used. During the implementation of the ANFIS, different combinations of the selected parameters were tested for their effects on the performance of fault detection. This methodology was implemented at a windfarm, commissioned in 2004, in five 2.3 MW fixed-speed onshore wind turbines equipped with a traditional servo-valve controlled hydraulic pitch system. Overall, data on 10 years of the operation of each wind turbine were utilised, and a total of nine pitch events were considered. Individual measurement for each blade angle was available for detecting pitch faults. Results demonstrated above 86% achievement of F1-score for pitch fault detection.

Suggested Citation

  • Korkos, Panagiotis & Linjama, Matti & Kleemola, Jaakko & Lehtovaara, Arto, 2022. "Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system," Renewable Energy, Elsevier, vol. 185(C), pages 692-703.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:692-703
    DOI: 10.1016/j.renene.2021.12.047
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    References listed on IDEAS

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    1. Ravi Pandit & David Infield, 2018. "Gaussian Process Operational Curves for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    3. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
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    Cited by:

    1. 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.
    2. Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    4. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    5. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.

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