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Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier

Author

Listed:
  • Lijun Zhang

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China)

  • Kai Liu

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China)

  • Yufeng Wang

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China)

  • Zachary Bosire Omariba

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
    Computer Science Department, Egerton University, Egerton 20115, Kenya)

Abstract

When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve fitted by normal sample data, are extracted to build the model based on the random forest classifier with the confusion matrix for result assessment. The model indicates that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition. This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed. Compared with other classification models, the model based on the random forest classifier is more accurate and more efficient in terms of computing capabilities, making it more suitable for the practical application on ice detection.

Suggested Citation

  • Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2548-:d:171765
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    References listed on IDEAS

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    5. Sudhakar Gantasala & Jean-Claude Luneno & Jan-Olov Aidanpää, 2016. "Influence of Icing on the Modal Behavior of Wind Turbine Blades," Energies, MDPI, vol. 9(11), pages 1-14, October.
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    Citations

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    Cited by:

    1. Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
    2. Yanpeng Hao & Zhaohong Yao & Junke Wang & Hao Li & Ruihai Li & Lin Yang & Wei Liang, 2019. "A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering," Energies, MDPI, vol. 12(24), pages 1-14, December.
    3. Zhijin Zhang & Hang Zhang & Xu Zhang & Qin Hu & Xingliang Jiang, 2024. "A Review of Wind Turbine Icing and Anti/De-Icing Technologies," Energies, MDPI, vol. 17(12), pages 1-34, June.
    4. Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
    5. Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    6. Samet Ozturk & Vasilis Fthenakis, 2020. "Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures," Energies, MDPI, vol. 13(5), pages 1-25, March.
    7. Xiyun Yang & Tianze Ye & Qile Wang & Zhun Tao, 2020. "Diagnosis of Blade Icing Using Multiple Intelligent Algorithms," Energies, MDPI, vol. 13(11), pages 1-15, June.
    8. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    9. 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.
    10. Ivan Kabardin & Sergey Dvoynishnikov & Maxim Gordienko & Sergey Kakaulin & Vadim Ledovsky & Grigoriy Gusev & Vladislav Zuev & Valery Okulov, 2021. "Optical Methods for Measuring Icing of Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-14, October.
    11. Morrison, Rory & Liu, Xiaolei & Lin, Zi, 2022. "Anomaly detection in wind turbine SCADA data for power curve cleaning," Renewable Energy, Elsevier, vol. 184(C), pages 473-486.
    12. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    13. 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.
    14. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    15. Gregory Duthé & Imad Abdallah & Sarah Barber & Eleni Chatzi, 2021. "Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades," Energies, MDPI, vol. 14(21), pages 1-33, November.
    16. Changzhi Li & Dandan Liu & Mao Wang & Hanlin Wang & Shuai Xu, 2023. "Detection of Outliers in Time Series Power Data Based on Prediction Errors," Energies, MDPI, vol. 16(2), pages 1-19, January.

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