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Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

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  • Jiménez, Alfredo Arcos
  • García Márquez, Fausto Pedro
  • Moraleda, Victoria Borja
  • Gómez Muñoz, Carlos Quiterio

Abstract

The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

Suggested Citation

  • Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:1034-1048
    DOI: 10.1016/j.renene.2018.08.050
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    Cited by:

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    5. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    6. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    7. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    8. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    9. 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.
    10. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Wang, Zixuan & Qin, Bo & Sun, Haiyue & Zhang, Jian & Butala, Mark D. & Demartino, Cristoforo & Peng, Peng & Wang, Hongwei, 2023. "An imbalanced semi-supervised wind turbine blade icing detection method based on contrastive learning," Renewable Energy, Elsevier, vol. 212(C), pages 251-262.
    12. 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.
    13. 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.
    14. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    15. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    16. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
    17. Ana María Peco Chacón & Isaac Segovia Ramírez & Fausto Pedro García Márquez, 2020. "False Alarms Analysis of Wind Turbine Bearing System," Sustainability, MDPI, vol. 12(19), pages 1-11, September.

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