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Blades icing identification model of wind turbines based on SCADA data

Author

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  • Dong, Xinghui
  • Gao, Di
  • Li, Jia
  • Jincao, Zhang
  • Zheng, Kai

Abstract

Blades icing would reduce the aerodynamic performance, cause power generation loss of WTGs, and even affect the safety of production and operation. By analyzing the relationship between blades icing and Supervisory Control And Data Acquisition (SCADA) data characteristic parameters at different stages in the process of wind turbines generating energy transfer, the paper calculated the accompanying changes in blades icing timing of Wind Turbine Generator System (WTGs) output power performance, mechanical performance and aerodynamic performance characteristic parameters. This paper then applies the residual to describe the deviation degree of each characteristic parameters value, and establishes the blades icing identification model by progressive parameterization judgment form. In addition, combined with the statistical properties of historical meteorological parameters of blades icing, the timely and accurate determination of blades icing was achieved. The identification results of the model are reliable and accurate through the verification of blades icing examples in different regions and of different wind turbines. The blades icing identification model based on SCADA data variation characteristics does not require adding new hardware and software investment, but it has even higher sensitivity. It can make accurate judgment in the early icing process, which is conducive to implement the control strategy and develop de-icing plan in advance.

Suggested Citation

  • Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    • Handle: RePEc:eee:renene:v:162:y:2020:i:c:p:575-586
    DOI: 10.1016/j.renene.2020.07.049
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    References listed on IDEAS

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    1. Wang, Yibing & Xu, Yuanming & Lei, Yuyong, 2018. "An effect assessment and prediction method of ultrasonic de-icing for composite wind turbine blades," Renewable Energy, Elsevier, vol. 118(C), pages 1015-1023.
    2. Wang, Qiang & Xiao, Jingping & Zhang, Tingting & Yang, Jianjun & Shi, Yu, 2020. "A new wind turbine icing computational model based on Free Wake Lifting Line Model and Finite Area Method," Renewable Energy, Elsevier, vol. 146(C), pages 342-358.
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    5. 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.
    6. Habibi, Hossein & Cheng, Liang & Zheng, Haitao & Kappatos, Vassilios & Selcuk, Cem & Gan, Tat-Hean, 2015. "A dual de-icing system for wind turbine blades combining high-power ultrasonic guided waves and low-frequency forced vibrations," Renewable Energy, Elsevier, vol. 83(C), pages 859-870.
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    Cited by:

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    2. Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    3. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    4. 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.
    5. 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.
    6. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    7. José Gibergans-Báguena & Pablo Buenestado & Gisela Pujol-Vázquez & Leonardo Acho, 2022. "A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems," Energies, MDPI, vol. 15(2), pages 1-27, January.
    8. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    9. 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).
    10. Chengming Zuo & Juchuan Dai & Guo Li & Mimi Li & Fan Zhang, 2023. "Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC," Energies, MDPI, vol. 16(6), pages 1-24, March.
    11. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Xiao Wang & Zheng Zheng & Guoqian Jiang & Qun He & Ping Xie, 2022. "Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network," Energies, MDPI, vol. 15(8), pages 1-19, April.

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