风电机组功率模型参数辨识及风资源利用率评估  

POWER MODEL PARAMETER IDENTIFICATION AND WIND RESOURCE UTILIZATION EVALUATION OF WIND TURBINE

作　　者：刘明宇 姚芳[1] 李志刚[1] 张彧硕 刘宁 黄凯[1] Liu Mingyu;Yao Fang;Li Zhigang;Zhang Yushuo;Liu Ning;Huang Kai(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300131,China;Experimental Training Center,Hebei University of Technology,Tianjin 300131,China;State Grid Jibei Electric Power Co.,Ltd.Zhangjiakou Power Supply Company,Zhangjiakou 075000,China)

机构地区：[1]省部共建电工装备可靠性与智能化国家重点实验室(河北工业大学),天津300131 [2]河北工业大学实验实训中心,天津300131 [3]国网冀北电力有限公司张家口供电公司,张家口075000

出　　处：《太阳能学报》2020年第12期305-315,共11页Acta Energiae Solaris Sinica

基　　金：国家科技支撑计划(2015BAA09B01);国家自然科学基金(51377044);河北省自然科学基金(E2017202284);河北省教育厅青年基金(QN2017316)。

摘　　要：基于风电机组功率特性五参数模型,引入灰狼优化算法和网格聚类算法辨识模型参数,实现风电机组功率特性的准确、快速建模。基于实际功率特性和风速分布特征,采用非参数核密度估计法建立各风速段的风资源利用率预测误差概率分布,提出计及不确定性的风电机组风资源利用率置信区间表征及估计方法。算例分析验证了风电机组功率模型参数辨识及风资源利用率评估方法的有效性。Based on the five-parameter model of wind turbine power characteristics,the grey wolf optimization algorithm and the grid clustering algorithm are introduced to identify the model parameters,realizing accurately and rapidly identification of wind turbine power model parameters. Based on actual power characteristics and wind speed distribution,the non-parametric kernel density estimation is adopted to establish the probability distribution of wind resource utilization prediction error in each wind speed segment,and the confidence interval representation and estimation method of wind resource utilization rate for wind turbines considering uncertainty is proposed. The effectiveness of wind turbine power model parameter identification and wind resource utilization evaluation methods are verified by the numerical example.

关 键 词：风电机组 参数辨识 聚类算法 风资源利用率 预测误差 置信区间 

分 类 号：TK89[动力工程及工程热物理—流体机械及工程]