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Diagnosis of Blade Icing Using Multiple Intelligent Algorithms

Author

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  • Xiyun Yang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Beijing 102206, China)

  • Tianze Ye

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Qile Wang

    (Zhong-Neng Power-Tech Development Co., Ltd., Beijing 100089, China)

  • Zhun Tao

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2975-:d:369411
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    References listed on IDEAS

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    1. 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.
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    1. 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).
    2. Swenson, Lauren & Gao, Linyue & Hong, Jiarong & Shen, Lian, 2022. "An efficacious model for predicting icing-induced energy loss for wind turbines," Applied Energy, Elsevier, vol. 305(C).
    3. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, 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. Valery Okulov & Ivan Kabardin & Dmitry Mukhin & Konstantin Stepanov & Nastasia Okulova, 2021. "Physical De-Icing Techniques for Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-16, October.
    6. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.

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