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  • Open Access


    Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

    Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

    Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854

    Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

  • Open Access


    Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

    Tingshun Li1, Jiaohui Xu1,*, Zesan Liu2, Dadi Wang2, Wen Tan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 767-782, 2023, DOI:10.32604/cmes.2022.020702

    Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are… More >

  • Open Access


    A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines

    Xiyang Li1,2, Bin Cheng1,2, Hui Zhang1,2,*, Xianghan Zhang1, Zhi Yun1

    Energy Engineering, Vol.118, No.6, pp. 1869-1886, 2021, DOI:10.32604/EE.2021.015542

    Abstract With the continuous increase in the proportional use of wind energy across the globe, the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consideration for research. Therefore, it is crucial to accurately analyze the thickness of icing on wind turbine blades, which can serve as a basis for formulating corresponding control measures and ensure a safe and stable operation of wind turbines in winter times and/or in high altitude areas. This paper fully utilized the advantages of the support vector machine (SVM) and back-propagation More >

  • Open Access


    Research on Effect of Icing Degree on Performance of NACA4412 Airfoil Wind Turbine

    Yuhao Jia1, Bin Cheng1,2,*, Xiyang Li1,2, Hui Zhang1,2, Yinglong Dong1

    Energy Engineering, Vol.117, No.6, pp. 413-427, 2020, DOI:10.32604/EE.2020.012019

    Abstract In order to study the effect of icing on the wind turbine blade tip speed ratio and wind energy utilization coefficient under working conditions, it is important to better understand the growth characteristics of wind turbine blade icing under natural conditions. In this paper, the icing test of the NACA4412 airfoil wind turbine was carried out using the natural low temperature wind turbine icing test system. An evaluation model of icing degree was established, and the influence of wind speed and icing degree on the performance parameters of wind turbines was compared and analyzed. It… More >

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