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265 / 2021-11-07 22:53:17
Research on wind turbine power curve modeling method based on regression analysis and prediction model
wind power curve; Quartile method; Data cleaning; Regression analysis prediction model
Renewable energy system > Renewable energy system planning, design, and operation
Draft Rejected
Yanfeng Tian / Shenyang University of Technology School of Electrical Engineering
Shun Wang / Shenyang University of Technology School of Electrical Engineering
Zhe Wang / Shenyang University of Technology School of Electrical Engineering
Yang Liu / Shenyang University of Technology School of Electrical Engineering
Zuoxia Xing / Shenyang University of Technology School of Electrical Engineering
Wind power curve plays an important role in wind power prediction, wind turbine condition monitoring, wind energy potential prediction and wind turbine selection. In the actual operation of wind turbine, abnormal values will appear in the original data due to accidents such as wind abandonment, power limitation and blade damage. These abnormal values often affect the accuracy of wind power curve modeling. Based on the quartile method, this paper cleans the abnormal value data of wind power, effectively eliminates the data deviating from the main concentration trend, and reduces the impact of abnormal data on the rationality of mathematical modeling. The gradient lifting regression analysis prediction model, KNN regression analysis prediction model, decision tree regression analysis prediction model, random forest regression analysis prediction model and extreme random forest regression analysis model are studied. The average absolute error, root mean square error and determination coefficient are used as evaluation indexes. The experimental results show that, Compared with other methods, the gradient lifting regression analysis prediction model has higher prediction accuracy and can accurately reflect the characteristics of the real wind power curve.

 
Important Date

  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
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