基于实测数据的风电功率曲线建模及不确定估计
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摘要
比较了最大值法、最大概率法和比恩法这3种风电功率曲线建模方法,指出利用比恩法绘制的基于实测现场数据的风电功率曲线,与风力发电机组的实际运行更吻合。对风电功率曲线的不确定因素进行分析,由于全局风功率分布并不满足某一特定分布,通过分区拟合对风速进行分级;再采用一种非参数区间估计方法,建立各风速等级的功率概率密度函数;在点估计的基础上求取风电功率曲线的不确定估计区间,使风电功率曲线具有较高的可靠性。算例验证了所提出方法的有效性及实用性。
Three methods of wind power curve modeling are compared:maximum value method,maximum probability method and method of bins. It is pointed out that the wind power curve drawn by the method of bins according to the measured field data better reflects the actual operation of wind turbine. The uncertainty factors of the wind power curve are analyzed and the wind speed is classified by the partitioned fitting because the global wind power does not satisfy a particular distribution. A method of non-parametric confidence interval estimation is applied to establish the probability density function of wind power for each wind speed level. The uncertainty estimation interval of wind power curve is obtained based on the point estimations to make the wind power curve more reliable. The example analysis proves the efficiency and feasibility of the proposed method.
Three methods of wind power curve modeling are compared:maximum value method,maximum probability method and method of bins. It is pointed out that the wind power curve drawn by the method of bins according to the measured field data better reflects the actual operation of wind turbine. The uncertainty factors of the wind power curve are analyzed and the wind speed is classified by the partitioned fitting because the global wind power does not satisfy a particular distribution. A method of non-parametric confidence interval estimation is applied to establish the probability density function of wind power for each wind speed level. The uncertainty estimation interval of wind power curve is obtained based on the point estimations to make the wind power curve more reliable. The example analysis proves the efficiency and feasibility of the proposed method.
引文
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[2]韩爽.风电场功率短期预测方法研究[D].北京:华北电力大学,2008.HAN Shuang.Study of short-term wind power prediction method[D].Beijing:North China Electric Power University,2008.
[3]范高锋,王伟胜,刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.FAN Gaofeng,WANG Weisheng,LIU Chun,et al.Wind power prediction based on artificial neural network[J].Proceedings of the CSEE,2008,28(34):118-123.
[4]刘立群,王志新,张华强,等.分布式风力发电系统MPPT控制[J].电力自动化设备,2011,31(8):6-10.LIU Liqun,WANG Zhixin,ZHANG Huaqiang,et al.MPPT control of distributed wind power generation system[J].Electric Power Automation Equipment,2011,31(8):6-10.
[5]赵亮,韩华玲,陈宁,等.基于模糊滑模控制的风电机组最大风能追踪策略[J].电力自动化设备,2012,32(12):74-79.ZHAO Liang,HAN Hualing,CHEN Ning,et al.Maximum wind energy tracking based on fuzzy sliding mode control[J].Electric Power Automation Equipment,2012,32(12):74-79.
[6]GAMESA EOLICA S A.Gamesa G52/G58-850 k W technical files[R].Vitoria,Canada:[s.n.],2001.
[7]郭琦.基于NWP的风电负荷预测方法在内蒙古电网中的应用[D].天津:天津大学,2010.GUO Qi.The application of NWP-based wind power load prediction method in Inner Mongolia Power Grid[D].Tianjin:Tianjin University,2010.
[8]郎斌斌,穆钢,严干贵,等.联网风电机组风速-功率特性曲线的研究[J].电网技术,2008,32(12):70-74.LANG Binbin,MU Gang,YAN Gangui,et al.Research on wind speed vs output power characteristic curve of wind power generator interconnected with power grid[J].Power System Technology,2008,32(12):70-74.
[9]RAUH A,ANAHUA E,BARTH S,et al.Phenomenological response theory to predict power output[C]∥Wind Energy:Proceedings of the Euromech Colloquium.Berlin,Germany:Springer,2007:153-158.
[10]李远.基于风能资源特征的风电机组优化选型方法研究[D].北京:华北电力大学,2008.LI Yuan.Method research on wind turbine optimal selection based on wind resource characteristics[D].Beijing:North China Electric Power University,2008.
[11]刘昊,柳亦兵,辛卫东,等.基于运行数据的风力发电机组功率特性分析[J].电网与清洁能源,2009,25(7):53-56.LIU Hao,LIU Yibing,XIN Weidong,et al.Wind turbine power performance based on the operation data[J].Power System and Clean Energy,2009,25(7):53-56.
[12]黎孟岩,刘兴杰,米增强.风力发电机组功率曲线建模方法研究[J].云南电力技术,2012,40(3):1-5.LI Mengyan,LIU Xingjie,MI Zengqiang.Research on modeling method of wind turbine power curve[J].Yunnan Electric Power,2012,40(3):1-5.
[13]KRZYZAK A,WALK H.A distribution-free theory of nonparametric regression[M].New York,USA:Springer-Verlag,2002:70-80.
[14]王彩霞,鲁宗相,乔颖,等.基于非参数回归模型的短期风电功率预测[J].电力系统自动化,2010,34(16):78-82,91.WANG Caixia,LU Zongxiang,QIAO Ying,et al.Short-term wind power forecast based on non-parametric regression model[J].Automation of Electric Power Systems,2010,34(16):78-82,91.
[15]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.ZHOU Songlin,MAO Meiqin,SU Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16.
[16]OLIVE D J.Prediction intervals for regression models[J].Computational Statistics&Data Analysis,2007,51(6):3115-3122.
[17]南晓强,李群湛.考虑风功率预测误差分布的储能功率与容量配置法[J].电力自动化设备,2013,33(11):117-122.NAN Xiaoqiang,LI Qunzhan.Energy storage power and capacity allocation based on wind power forecasting error distribution[J].Electric Power Automation Equipment,2013,33(11):117-122.