Probabilistic load flow calculation of power system integrated wind power based on CV-KDE

被引：0
|
作者：
Zhang X. [1 ]
Gao T. [1 ]
Wang K. [2 ]
Chen W. [1 ]
Wang X. [1 ]
Hu K. [1 ]
机构：
[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou
[2] State Grid Gansu Electric Power Company Electric Power Research Institute, Lanzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 09期
关键词：
Cross validation; Kernel density estimation; Probabilistic load flow calculation; Probability distribution; Statistical method; Wind power;
D O I：
10.19912/j.0254-0096.tynxb.2019-0831
中图分类号：
学科分类号：
摘要：
Wind power of the high proportion connected to the power grid exacerbates the uncertainty and randomness of the power system, so the probabilistic load flow calculation for system uncertainty analysis is particularly important. However, the wind power probability model used in the existing probabilistic load flow calculation has the disadvantages of hypothesis parameter distribution and the inability to fully consider the influence of various random factors, resulting in large errors in the load flow calculation results. In this paper, the non-parametric kernel density estimation considering boundary correction is used to establish the wind power probability model in probabilistic load flow calculation, and the grid search method with Cross Validation(CV) is used to optimize the bandwidth parameters of kernel density estimation. The model uses the grid search method to test and train the data to obtain the optimal solution, so that the bandwidth parameters are more accurate than the parameters obtained by the traditional bandwidth solving method, and the data utilization is more enough. Finally, the accuracy and validity of the proposed kernel density estimation probability model and bandwidth solution method are verified by load flow simulation analysis of the improved IEEE 39 node power system integrated wind power. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：263 / 269
页数：6
相关论文
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