摘要
年负荷序列聚类是年负荷序列建模及场景生成的基础。针对现有方法在波动性特征提取方面的不足,本文提出了一种基于HP滤波器及重标极差法的年负荷序列聚类方法。首先,采用HP滤波器对跨度为一个季节的日负荷序列进行滤波,得到一个波动项和趋势项独立的随机过程。其次,针对趋势项,利用日负荷相关特征的概念提取特征;针对波动项,采用重标极差法来提取每日波动项的特征。最后,将趋势项特征和波动项特征结合作为总特征,并采用模糊C均值(FCM)聚类算法进行聚类。采用本文方法对某国家年负荷序列进行聚类,结果表明该方法具有一定的优越性,并能一定程度地反映日负荷序列的波动特性。
Annual load series clustering is the basis of modeling and scenario generation of annual load series.In view of the shortcomings of existing clustering methods in terms of feature extraction for load fluctuation characteristics,a method of daily load series clustering based on HP filter and rescaled range analysis is proposed.Firstly,the HP filter is used on load series spanning a season to obtain a random process with independent trend component and fluctuation component.Secondly,the concept of daily load related features is used to extract the features of the trend component.The features of the fluctuation component are extracted based on rescaled range analysis.Then,load series are clustered using the fuzzy C-means algorithm with the total feature combined with the trend component features and the fluctuation component features.The annual load series of a country are clustered using the proposed method.Results show that the method has certain advantages and can reflect the characteristics of daily load fluctuation to a certain extent.
作者
马涛
MA Tao(Electrical&Instrumentation Center,SINOPEC Yangzi Petrochemical Co.,Ltd.,Nanjing 210044,China)
关键词
电气工程
HP滤波器
重标极差法
趋势项
波动项
负荷序列聚类
electrical engineering
HP filter
rescaled range analysis
trend component
fluctuation component
load series clustering
