Ice Accretion Prediction on Wind Turbines and Consequent Power Losses

被引:37
|
作者
Yirtici, Ozcan [1 ]
Tuncer, Ismail H. [1 ]
Ozgen, Serkan [1 ]
机构
[1] METU, Dept Aerosp Engn, TR-06800 Ankara, Turkey
关键词
D O I
10.1088/1742-6596/753/2/022022
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Ice accretion on wind turbine blades modifies the sectional profiles and causes alteration in the aerodynamic characteristic of the blades. The objective of this study is to determine performance losses on wind turbines due to the formation of ice in cold climate regions and mountainous areas where wind energy resources are found. In this study, the Blade Element Momentum method is employed together with an ice accretion prediction tool in order to estimate the ice build-up on wind turbine blades and the energy production for iced and clean blades. The predicted ice shapes of the various airfoil profiles are validated with the experimental data and it is shown that the tool developed is promising to be used in the prediction of power production losses of wind turbines.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Power production prediction of wind turbines using a fusion of MLP and ANFIS networks
    Morshedizadeh, Majid
    Kordestani, Mojtaba
    Carriveau, Rupp
    Ting, David S. -K.
    Saif, Mehrdad
    IET RENEWABLE POWER GENERATION, 2018, 12 (09) : 1025 - 1033
  • [42] PREDICTION OF AERODYNAMIC LOADINGS AND POWER PRODUCTIONS OF WIND TURBINES IN WAKE BY NUMERICAL SIMULATION
    Zhou, Nina
    Gao, Xiangyu
    Chen, Jun
    ASME FLUIDS ENGINEERING DIVISION SUMMER MEETING - 2014, VOL 1D: SYMPOSIA, 2014,
  • [43] An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions
    Vahidzadeh, Mohsen
    Markfort, Corey D.
    ENERGIES, 2020, 13 (04)
  • [44] CNN-LSTM-AM: A power prediction model for offshore wind turbines
    Sun, Yu
    Zhou, Qibo
    Sun, Li
    Sun, Liping
    Kang, Jichuan
    Li, He
    OCEAN ENGINEERING, 2024, 301
  • [45] Health Assessment Methods for Wind Turbines Based on Power Prediction and Mahalanobis Distance
    Zhan, Jun
    Wang, Ronglin
    Yi, Lingzhi
    Wang, Yaguo
    Xie, Zhengjuan
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2019, 33 (02)
  • [46] Wind direction prediction for yaw control of wind turbines
    Dongran Song
    Jian Yang
    Yao Liu
    Mei Su
    Anfeng Liu
    Young Hoon Joo
    International Journal of Control, Automation and Systems, 2017, 15 : 1720 - 1728
  • [47] Wind Direction Prediction for Yaw Control of Wind Turbines
    Song, Dongran
    Yang, Jian
    Liu, Yao
    Su, Mei
    Liu, Anfeng
    Joo, Young Hoon
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2017, 15 (04) : 1720 - 1728
  • [48] Wind Gust Detection and Impact Prediction for Wind Turbines
    Zhou, Kai
    Cherukuru, Nihanth
    Sun, Xiaoyu
    Calhoun, Ronald
    REMOTE SENSING, 2018, 10 (04)
  • [49] Predictions of ice formations on wind turbine blades and power production losses due to icing
    Yirtici, Ozcan
    Ozgen, Serkan
    Tuncer, Ismail H.
    WIND ENERGY, 2019, 22 (07) : 945 - 958
  • [50] The ultra-short term power prediction of wind farm considering operational condition of wind turbines
    Fang, Ruiming
    Wang, Yandong
    Shang, Rongyan
    Liang, Yin
    Wang, Li
    Peng, Chongqing
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2016, 41 (35) : 15733 - 15739