引用本文:王钤,潘险险,陈迎,等.基于实测数据的风电场风速-功率模型的研究[J].电力系统保护与控制,2014,42(2):23-27.
WANG Qian,PAN Xian-xian,CHEN Ying,et al.Study of wind speed-active power model for wind farm based on measured data[J].Power System Protection and Control,2014,42(2):23-27
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基于实测数据的风电场风速-功率模型的研究
王钤1, 潘险险2, 陈迎3, 杨汾艳1, 林俐2
1.广东电网公司电力科学研究院,广东 广州 510080;2.新能源电力系统国家重点实验室(华北电力大学),北京 102206;3.安徽省电力公司检修公司,安徽 合肥 230000
摘要:
建立准确的风电场模型是风电接入系统相关研究的基础。首先通过对某双馈风电机组的标准功率特性曲线和实测风速-功率散点图进行对比,针对它们之间的差异问题,建立基于实测运行数据的风电机组风速-功率模型。其次,针对地形复杂、机组排列不规则的大型风电场风速差异性问题,利用K-means聚类算法对风电场内所有风电机组按实测风速数据进行聚类划分,建立了整个风电场的等效风速模型,进而给出了基于实测运行数据的风电场风速-功率模型。然后,以某实际风电场为例,对该风电场内的风电机组按风速进行K-means聚类划分,结果显示该划分结果与简单按地理位置的机群划分结果有明显差异。最后,对传统的风速-功率模型和所提出的风速-功率模型输出结果进行比较,结果证明所提出的模型相对于传统模型而言,准确性有了较大的提高。
关键词:  风速-功率模型  实测数据  K-means聚类算法  聚类划分  风电场建模
DOI:10.7667/j.issn.1674-3415.2014.02.004
分类号:
基金项目:国家科技支撑计划(2013BAA02B00)
Study of wind speed-active power model for wind farm based on measured data
WANG Qian1, PAN Xian-xian2, CHEN Ying3, YANG Fen-yan1, LIN Li2
1.Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China;2.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China;3.Maintenance Company of Anhui Electric Power Corporation, Hefei 230000, China
Abstract:
Accurate model of the wind farm is the basis for the analysis of wind power integrating grid. Firstly, the comparison is made between the standard power characteristic curve and the measured scatter plot of wind speed-active power for DFIG. The wind speed-active power model is proposed based on the measured data. For the wind speed differences of wind turbines in large wind farm caused by complex terrain and the crew irregular arrangement, K-means clustering algorithm is used for the equivalent wind speed model of the whole wind farm. Then the wind speed-active power model of wind farm based on measured data is proposed. Taking an actual wind farm as example, K-means clustering algorithm is used to the clusters division. Result shows that the division by wind speed is different from that by location of wind turbine units. Finally, to verify the effectiveness of the new wind speed-active power model, the error comparative analysis is made between the new model and the traditional model. Result shows that the accuracy of the model is greatly improved with the proposed method.
Key words:  wind speed-active power model  measured data  K-means clustering algorithm  clusters division  wind farm modeling
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