Research on Modeling of Wind Turbine based on LS-SVM

被引:0
|
作者
Yang, Xiyun [1 ]
Cui, Yuqi [1 ]
Zhang, Hongsheng [2 ]
Tang, Ningning [1 ]
机构
[1] North China Elect Power Univ, Dept Automate, Beijing 102206, Peoples R China
[2] Inner Mongolia Jingke Power Co Ltd, Neimenggu PT-028000, Peoples R China
基金
中国国家自然科学基金;
关键词
modeling; LS-SVM; cure fitting; wind turbine;
D O I
暂无
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
The generator power have related with the wind turbine torque heavily. The wind speed, the rotor speed, the pitch angle and the inherent parameters of wind turbine can influence the wind turbine torque. Different torque curves in different operation can be simulated by Simulink software of Matlab. However, this method needs wind turbine's parameters which aren't usually obtained to construct the model and simplifying of the model process maybe bring with errors. The paper proposed-an intelligent torque model with LS-SVM algorithm, this model only needs the input-output training samples to overcome the problem of parameters unavailable. Compared with BP Network, this algorithm supports the training of small samples and has good abilities in calculating speed, approximating precision and forecasting effects. Simulation results demonstrate the validity of the model. Simulation based on practical data of wind power is also given and can provide a beneficial reference for the prediction theory of wind turbine.
引用
收藏
页码:1207 / +
页数:3
相关论文
共 50 条
  • [1] Research on Fault Diagnosis Method of Wind Turbine Based on Wavelet Analysis and LS-SVM
    Liu, Changliang
    Qi, Weixue
    [J]. APPLIED ENERGY TECHNOLOGY, PTS 1 AND 2, 2013, 724-725 : 593 - 597
  • [2] Power Prediction Research of Wind Farm Based on LS-SVM Multi-model Modeling
    Chen, Bei
    [J]. PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MANUFACTURING ENGINEERING AND INTELLIGENT MATERIALS (ICMEIM 2017), 2017, 100 : 619 - 624
  • [3] Research on LS-SVM wind speed prediction method based on PSO
    [J]. 1600, Chinese Society for Electrical Engineering (36):
  • [4] Research on Fault Warning of Doubly Fed Wind Power Generator based on LS-SVM
    Niu, Sheng-Yu
    Liu, Bo-Wen
    Zhang, Xin-Yan
    [J]. PROCEEDINGS OF THE 3RD ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2017), 2017, 131 : 158 - 163
  • [5] Sensor dynamic modeling based on LS-SVM and NGA
    Wang, Q.
    Feng, Z.
    Shida, K.
    [J]. Key Engineering Materials, 2008, 381-382 : 439 - 442
  • [6] Modeling for electric characteristic of SOFC based on LS-SVM
    Huo, Haibo
    Zhu, Xinjian
    Cao, Guangyi
    [J]. Gaojishu Tongxin/Chinese High Technology Letters, 2007, 17 (08): : 836 - 839
  • [7] The research of gas prediction based on LS-SVM and ICA
    [J]. Gong, Xingyu, 1600, Binary Information Press (10):
  • [8] Research on the video advertising detection based on LS-SVM
    Lan, Xiao-Ling
    Zhang, Shutuan
    [J]. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10 (07): : 363 - 374
  • [9] Online optimal Modeling of LS-SVM based on time window
    Zhu, Y. F.
    Wan, P.
    Zhang, Y.
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2006, 13E : 856 - 859
  • [10] Compensation of FOG temperature drift based on LS-SVM modeling
    Li, Nan
    Chen, Jiabin
    Yuan, Yan
    Han, Yongqiang
    Tian, Xiaochun
    [J]. PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 5515 - 5518