电力系统自动化2016，Vol.40Issue(9)：16-22,55,8.DOI:10.7500/AEPS20151008006
基于Copula理论的光伏功率高比例异常数据机器识别算法
Copula Theory Based Machine Identification Algorithm of High Proportion of Outliers in Photovoltaic Power Data
摘要
Abstract
In many photovoltaic (PV) power plants,problems of communication errors,equipment failures and PV power curtailment result in high proportion of outliers in measured PV power data,which is difficult for performance analysis of PV power plants and application of power data.Hence,a new identification methodology is proposed.A probabilistic PV power curve model is proposed based on Copula theory to describe the relationship between the measured global radiation and PV power.Based on engineering experiences and the characteristics of high dispersion,strong randomness and a large proportion of outliers,machine identification models are proposed to identify three typical types of outliers.These methods are verified using measured data of PV power plants and artificial data.The effectiveness of applying the outlier identification methods is investigated through a day-ahead PV power forecasting application.Moreover,the proposed machine identification method is more adaptable and have higher accuracy than the conventional 3-sigma method.关键词
光伏功率/高比例异常数据/概率功率曲线/Copula理论/机器识别Key words
photovoltaic power/high proportion of outliers/probabilistic power curve/Copula theory/machine identification引用本文复制引用
龚莺飞,鲁宗相,乔颖,王强,曹欣..基于Copula理论的光伏功率高比例异常数据机器识别算法[J].电力系统自动化,2016,40(9):16-22,55,8.基金项目
国家科技支撑计划资助项目(2013BAA01B03) （2013BAA01B03）
国网河北省电力公司项目(SGHB0000DJK1400084)。This work is supported by National Key Technologies R&D Program(No.2013BAA01B03) （SGHB0000DJK1400084）
