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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。
The traditional measure-correlate-predict (MCP) method in wind energy resource assessment of wind farms can only predict the long-term wind resource of the target station using a single set of reference data, but the precision is poor. In order to make full use of the information contained in multiple sets of reference data, this paper proposes to introduce the correlation between wind speed sequences to measure the trend of wind speed fluctuation between reference station and target station, Based on the weight distribution, a portfolio model of wind energy resources assessment based on the volatility cross-correlation coefficient was established. The long-term wind speed distribution of the target station is combined and predicted based on the correlation of the wind speed between stations. The long-term wind power density distribution and annual average wind power density of the target station are estimated. The results show that compared with the linear correlation coefficient, the cross correlation coefficient can measure the correlation of the wind speed more effectively. Taking the cross correlation coefficient as the weight parameter of the multi-reference station combination prediction, Wind energy resources for effective assessment.