Data-Driven Methods for the Analysis of Wind Turbine Yaw Control Optimization

被引:3
|
作者
Astolfi, Davide [1 ]
Castellani, Francesco [1 ]
Natili, Francesco [1 ]
机构
[1] Univ Perugia, Dept Engn, Via G Duranti 93, I-06125 Perugia, Italy
关键词
wind turbine; clean energy; efficiency; energy; renewable; sustainability; wind; LAYOUT;
D O I
10.1115/1.4047413
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
Multi-megawatt wind turbines are nowadays a mature technology, and therefore, there is considerable scientific and industrial attention to the opportunity of further improving the efficiency of wind kinetic energy conversion into electricity. One of the major developments in this field of research regards the optimization of wind turbine control. This work deals with a test case of yaw control optimization on a 2-MW wind turbine sited in Italy. The objective of the work is to compute the performance improvement provided by the upgrade after some months of operation. This has been accomplished through the formulation of an appropriate model for the power of the wind turbine of interest and the analysis of the residuals between model estimates and measurements before and after the upgrade. In this work, a general procedure for selecting a robust multivariate linear model is adopted, and the resulting model, employing as input variables several operational variables from the nearby wind turbines in the farm, is used for quantifying the performance improvement. The estimate is that this upgrade provides a 0.8% improvement of the annual energy production.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Data-driven adaptive optimization of wind turbine yaw parameters
    Liu, Yingming
    Chen, Liang
    Wang, Xiaodong
    Xie, Hongfang
    [J]. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2022, 43 (08): : 366 - 372
  • [2] A Data-Driven Model for Wind Plant Power Optimization by Yaw Control
    Gebraad, P. M. O.
    Teeuwisse, F. W.
    van Wingerden, J. W.
    Fleming, P. A.
    Ruben, S. D.
    Marden, J. R.
    Pao, L. Y.
    [J]. 2014 AMERICAN CONTROL CONFERENCE (ACC), 2014, : 3128 - 3134
  • [3] A Data-Driven Approach for Identification and Compensation of Wind Turbine Inherent Yaw Misalignment
    Bao, Yunong
    Yang, Qinmin
    Li, Siliang
    Miao, Kuangwei
    Sun, Youxian
    [J]. PROCEEDINGS 2018 33RD YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), 2018, : 961 - 966
  • [4] Optimization of Wind Turbine Performance With Data-Driven Models
    Kusiak, Andrew
    Zhang, Zijun
    Li, Mingyang
    [J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2010, 1 (02) : 66 - 76
  • [5] Optimization of the Yaw Control Error of Wind Turbine
    Liu, Yan
    Liu, Shu
    Zhang, Lihong
    Cao, Fuyi
    Wang, Liming
    [J]. FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [6] Improved Data-Driven Yaw Misalignment Calibration of Wind Turbine via LiDAR Verification
    Qu, Chenzhi
    Lin, Zhongwei
    Han, Xiangyu
    Wang, Chuanxi
    Wu, Quan
    Li, Xiongwei
    Zhang, Zonghui
    Gong, Yanfeng
    Jiang, Guangwen
    [J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 5611 - 5616
  • [7] A data-driven machine learning approach for yaw control applications of wind farms
    Santoni, Christian
    Zhang, Zexia
    Sotiropoulos, Fotis
    Khosronejad, Ali
    [J]. THEORETICAL AND APPLIED MECHANICS LETTERS, 2023, 13 (05)
  • [8] Control of wind turbine power and vibration with a data-driven approach
    Kusiak, Andrew
    Zhang, Zijun
    [J]. RENEWABLE ENERGY, 2012, 43 : 73 - 82
  • [9] Online data-driven approach of yaw error estimation and correction of horizontal axis wind turbine
    Xue, Jianguo
    Wang, Lu
    [J]. JOURNAL OF ENGINEERING-JOE, 2019, (18): : 4937 - 4940
  • [10] Wind Turbine Modeling With Data-Driven Methods and Radially Uniform Designs
    Tan, Matthias
    Zhang, Zijun
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2016, 12 (03) : 1261 - 1269