CAJ | 학술논문

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。
풍전장적역사운행수거우기시풍속화풍전공솔수거대풍전장적운행관리화전력계통적운행조도도구유중요의의。재실제운행중，풍전장적기풍현상교위엄중，기풍회도치풍속-공솔산점도중존재대량횡향분포적퇴적형이상수거，저회대구조풍전장적등치공솔곡선산생교대적영향，종이강저풍전공솔예측정도，진이대풍전장적운행관리화전력계통적운행조도조성불리영향。문중재분석풍전장기풍이상수거특정적기출상，제출일충기우사분위법화취류분석적이상수거조합사선모형，수선채용량차사분위법척제상규적분산형이상수거，재사용취류법척제퇴적형이상수거，병채용이차취류적사상래해결k-means취류중k 적취치문제。산례분석표명，해모형가유효척제기풍조성적이상수거족，대불동적풍전궤조화풍전장유교강적통용성，구유일정적공정실용개치。
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.