Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests

被引:64
作者
Park, Joon-Young [1 ]
Lee, Jae-Kyung [1 ]
Oh, Ki-Yong [2 ]
Lee, Jun-Shin [1 ]
机构
[1] Korea Elect Power Corp, KEPCO Res Inst, Future Technol Lab, Taejon 305380, South Korea
[2] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
关键词
Alarm generation; condition monitoring; fault data queue; power curve; turbine monitoring; wind turbine;
D O I
10.1109/TEC.2013.2294893
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
A novel power curve monitoring method for wind turbines was developed to prevent a turbine failure in a wind farm. Compared with the existing methods, this algorithm automatically calculates the power curve limits for power curve monitoring, even when a considerable number of abnormal data are included in wind speed-output power data measured at a wind turbine. In addition, the proposed algorithm automatically generates an alarm message when the wind speed-power data measured at the wind turbine deviate from the power curve limits, particularly considering their degree of deviation from the power curve limits and the cases when the measured data hover between the Warning Zones and the Alarm Zones. We confirmed its effectiveness through its field tests.
引用
收藏
页码:119 / 128
页数:10
相关论文
共 50 条
  • [1] Improved power curve monitoring of wind turbines
    Morshedizadeh M.
    Kordestani M.
    Carriveau R.
    Ting D.S.K.
    Saif M.
    Carriveau, Rupp (rupp@uwindsor.ca), 2017, SAGE Publications Inc., United States (41) : 260 - 271
  • [2] Implementation of novel hybrid approaches for power curve modeling of wind turbines
    Yesilbudak, Mehmet
    ENERGY CONVERSION AND MANAGEMENT, 2018, 171 : 156 - 169
  • [3] Power Curve-Based Fault Detection Method for Wind Turbines
    Bilendo, Francisco
    Badihi, Hamed
    Lu, Ningyun
    Cambron, Philippe
    Jiang, Bin
    IFAC PAPERSONLINE, 2022, 55 (06): : 408 - 413
  • [4] A novel power curve prediction method for horizontal-axis wind turbines using artificial neural networks
    Tai V.C.
    Tan Y.C.
    Rahman N.F.A.
    Chia C.M.
    Zhakiya M.
    Saw L.H.
    Energy Engineering: Journal of the Association of Energy Engineering, 2021, 118 (03): : 507 - 516
  • [5] Wind turbines power curve variability
    Khalfallah, Mohammed G.
    Koliub, Aboelyazied M.
    DESALINATION, 2007, 209 (1-3) : 230 - 237
  • [6] The ideal power curve of small wind turbines from field data
    Trivellato, F.
    Battisti, L.
    Miori, G.
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2012, 107 : 263 - 273
  • [7] A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines
    Bilendo, Francisco
    Badihi, Hamed
    Lu, Ningyun
    Cambron, Philippe
    Jiang, Bin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [8] A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network
    Tian, Xiange
    Jiang, Yongjian
    Liang, Chen
    Liu, Cong
    Ying, You
    Wang, Hua
    Zhang, Dahai
    Qian, Peng
    ENERGIES, 2022, 15 (18)
  • [9] Review of power curve modelling for wind turbines
    Carrillo, C.
    Obando Montano, A. F.
    Cidras, J.
    Diaz-Dorado, E.
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 21 : 572 - 581
  • [10] Assessment of Power Curve Fitting Performance of Parametric Models for Wind Turbines
    Yesilbudak, Mehmet
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2025, 15 (01): : 22 - 29