中文摘要：

风电场的历史运行数据尤其是风速和风电功率数据对风电场的运行管理和电力系统的运行调度都具有重要意义。在实际运行中,风电场的弃风现象较为严重,弃风会导致风速—功率散点图中存在大量横向分布的堆积型异常数据,这会对构造风电场的等值功率曲线产生较大的影响,从而降低风电功率预测精度,进而对风电场的运行管理和电力系统的运行调度造成不利影响。文中在分析风电场弃风异常数据特征的基础上,提出一种基于四分位法和聚类分析的异常数据组合筛选模型,首先采用两次四分位法剔除常规的分散型异常数据,再使用聚类法剔除堆积型异常数据,并采用二次聚类的思想来解决k-means聚类中k的取值问题。算例分析表明,该模型可有效剔除弃风造成的异常数据簇,对不同的风电机组和风电场有较强的通用性,具有一定的工程实用价值。

英文摘要：

The historical operating data collected from wind farms,especially wind and power data,is significantly important for operation and management of wind farms and scheduling of a power system.However,wind curtailments are severe in practical operations of wind farms,causing large amounts of stacked abnormal data clusters distributed horizontally in a wind-power scatter diagram.This kind of data leads to large errors in an equivalent power curve and inaccurate wind power prediction,affecting wind farm management and power system scheduling.According to the characteristics of abnormal data, this paper presents a combined model for eliminating abnormal data based on the quartile method and cluster analysis.The quartile method is used twice to eliminate scattered abnormal data and cluster analysis is then used to eliminate the stacked abnormal data.Moreover,the problem brought about by“k"value selection in k-means clustering is solved by a novel“re-cluster"method.A case study shows that the model presented is efficient for eliminating abnormal data clusters and can often be used for both wind turbines and wind farms for its practical advantages.