基于Copula理论的风电功率缺失数据补齐方法研究  

Study on wind power data completion method based on Copula theory

作　　者：杨茂[1] 马剑[1] Yang Mao;Ma Jian(School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin, China)

机构地区：[1]东北电力大学电气工程学院,吉林吉林132012

出　　处：《电测与仪表》2018年第3期13-19,共7页Electrical Measurement & Instrumentation

基　　金：国家重点基础研究发展计划项目(973计划)(2013CB228201);国家自然科学基金项目(51307017);吉林省产业技术与专项开发项目(2014Y124)

摘　　要：风电数据的完整性对风电功率的准确预测以及风能的利用具有重要意义。文中从风电场中风机输出功率的相关性分析,提出利用Copula理论建立不同风机输出功率的联合概率分布模型的方法。利用实测的风电功率数据,采用非参数核密度估计法估计风机输出功率的概率分布。以Kendall秩相关系数作为相关性测度。利用风机输出功率的相关性对缺失数据进行补齐。仿真实验说明补齐数据的准确率和平均相对误差得到较好的效果,能够有效提高补齐数据的质量。The integrity of wind power data is of great significance for the accurate prediction of wind power and the utilization of wind energy. Based on the correlation analysis of the output power of the wind farm, this paper proposes a joint probability distribution model of the output power in different wind turbines by using Copula theory. By using the measured wind power data, the probability distribution of wind power output is estimated through the non-parametric and density estimation method. Kendall rank correlation coefficient is used as the correlation measurement. The correlation between different wind turbines is used to complete missing data. Simulation experiments illustrate the accuracy and average relative error of completed data obtains good effect, and the quality of the completed data is improved effectively.

关 键 词：风电功率 COPULA理论 相关性分析 数据补齐 

分 类 号：TM614[电气工程—电力系统及自动化]