电力系统自动化Issue(21)：39-46,8.DOI:10.7500/AEPS20131213010
风电场弃风异常数据簇的特征及处理方法
Characteristics and Processing Method of Abnormal Data Clusters Caused by Wind Curtailments in Wind Farms
摘要
Abstract
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.关键词
风电功率曲线/弃风/异常数据/四分位法/聚类分析Key words
wind power curve/wind curtailment/abnormal data/quartile method/cluster analysis引用本文复制引用
赵永宁,叶林,朱倩雯..风电场弃风异常数据簇的特征及处理方法[J].电力系统自动化,2014,(21):39-46,8.基金项目
国家自然科学基金资助项目(51174290,51477174) （51174290,51477174）
高等学校博士学科点专项科研基金(博导类)资助项目(20110008110042)。This work is supported by National Natural Science Foundation of China(No.51174290,No.51477174)and Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP)of China(No.20110008110042) （博导类）
