基于波动互相关系数的风能资源评估组合模型

发布时间：2018-04-23 10:46

本文选题：风能资源评估 + 测量　；参考：《中国电机工程学报》2017年03期

【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。
[Abstract]:In the traditional wind energy resource assessment of wind farm, the measurement-correlate-prediction MCPs can only use single reference data to predict the long-term wind resources of the target station, but the accuracy is poor. In order to make full use of the information contained in multiple reference data, this paper proposes to introduce the correlation number between wind speed series to measure the correlation of wind speed fluctuation trend between reference station and target station. As a basis of weight distribution for multi-reference station combination prediction, a wind energy resource evaluation combination model based on fluctuation correlation number is established. Combined with the correlation of wind speed between stations, the long-term wind power density distribution and the annual average wind power density of the target station are estimated. The results show that, compared with the linear correlation coefficient, the interrelation number of waves can be used to measure the correlation of wind speed between stations more effectively, and the interrelation number of waves can be regarded as the weight parameter of multiple reference station combination prediction. The wind energy resource of wind farm can be evaluated more accurately.
【作者单位】：中国农业大学信息与电气工程学院;国网浙江省电力公司湖州供电公司;
【基金】：国家自然科学基金项目(51677188,51174290)~~
【分类号】：TM614;TK81

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