基于波动互相关系数的风能资源评估组合模型  

A Combined Wind Resource Assessment Model Based on Fluctuation Cross-Correlation Coefficient

作　　者：叶林[1] 饶日晟 杨丹萍[2] 靳晶新 张亚丽[1] 

机构地区：[1]中国农业大学信息与电气工程学院,北京市海淀区100083 [2]国网浙江省电力公司湖州供电公司,浙江省湖州市313000

出　　处：《中国电机工程学报》2017年第3期712-719,共8页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51677188;51174290)~~

摘　　要：传统的风电场风能资源评估中测量–关联–预测(measure-correlate-predict,MCP)方法只能利用单组参考数据预测目标站长期风资源,但精度较差。为了充分利用多组参考数据所包含的信息,该文提出引入风速序列间的波动互相关系数来衡量参考站和目标站之间风速波动趋势的相关程度,并将其作为多参考站组合预测的权重分配依据,建立了基于波动互相关系数的风能资源评估组合模型。结合站间风速的相关性对目标站长期风速分布进行了组合预测,估算了目标站长期风功率密度分布和年平均风功率密度。研究结果表明,与线性相关系数相比,波动互相关系数可以更有效地衡量站间风速的相关性;将波动互相关系数作为多参考站组合预测的权重参数,可以更准确地对风电场的风能资源状况进行有效评估。Traditional methods of measure-correlate-predict（MCP） for wind resource assessment of wind farms are insufficient to predict the long-term wind resource of the target site, due to the limitation of involving only one dataset from reference station. To fully make use of information contained in the datasets from multiple reference stations, fluctuation cross-correlation coefficient was introduced in this paper to measure the correlation of wind speed fluctuation trends between target site and reference site. A combined MCP model for wind resource assessment of wind farms based on fluctuation cross-correlation coefficient was then proposed by determining the weights of multiple reference stations according to the fluctuation cross-correlation coefficient. Ratio variation method was selected as MCP methods in the case study, and the long-term wind speed distribution was predicted by combination model. Then the wind power density distribution and annual wind power density at the target wind farm were assessed. Results indicate that fluctuation cross-correlation coefficient in comparison with linear correlation coefficient can effectively characterize the correlation between the reference site and target site, based on the combined MCP method which can improve the accuracy of the wind resource assessment.

关 键 词：风能资源评估 测量–关联–预测 波动互相关系数 风电场 组合模型 

分 类 号：TM71[电气工程—电力系统及自动化]