文章摘要
赵天辉,王建学,马龙涛,等.基于非参数回归分析的工业负荷异常值识别与修正方法[J].电力系统自动化,2017,41(18):53-59. DOI: 10.7500/AEPS20170118002.
ZHAO Tianhui,WANG Jianxue,MA Longtao, et al.Outlier Detection and Correction Method for Industrial Loads Based on Nonparametric Regression Analysis[J].Automation of Electric Power Systems,2017,41(18):53-59. DOI: 10.7500/AEPS20170118002.
基于非参数回归分析的工业负荷异常值识别与修正方法
Outlier Detection and Correction Method for Industrial Loads Based on Nonparametric Regression Analysis
DOI:10.7500/AEPS20170118002
关键词: 负荷管理  模式分类  异常数据识别  非参数回归分析
KeyWords: load management  pattern classification  outlier detection  nonparametric regression analysis
上网日期:2017-07-28
基金项目:陕西省重点研发计划重点产业创新链资助项目(2017ZDCXL-GY-02-03)
作者单位E-mail
赵天辉 陕西省智能电网重点实验室(西安交通大学电气工程学院), 陕西省西安市 710049  
王建学 陕西省智能电网重点实验室(西安交通大学电气工程学院), 陕西省西安市 710049 jxwang@mail.xjtu.end.cn 
马龙涛 国网铜川供电公司, 陕西省铜川市 727000  
朱宇超 陕西省智能电网重点实验室(西安交通大学电气工程学院), 陕西省西安市 710049  
摘要:
      工业负荷数据记录了用户的用电模式以及电量需求水平等重要信息,但是会因为干扰而导致记录数据中掺杂有异常值。针对上述问题,文中提出了利用非参数回归理论对工业用户负荷异常值展开辨析和更正。首先,考虑负荷数据时序相关特性,采用统计模糊矩阵分类法,对工业用户负荷进行用电模式分类,将负荷数据分为基础用电模式数据集和特殊用电模式数据集。然后,利用基础用电模式数据集,考虑各时刻的负荷数值分布情况,通过非参数回归分析方法提取中心负荷向量,进而构造异常数据域,对负荷异常值进行识别。最后,在常规加权均值法的基础上,引入负荷水平映射关系,完成对负荷异常值的修正。算例采用实际工业负荷数据进行测试,结果表明了所提方法的准确性。
Abstract:
      In a power system, the information on power consumption patterns and electricity demand levels is recorded in industrial load curves, part of which, however, will be abnormal because of unexpected interference. Therefore, a method based on nonparametric regression theory is proposed to detect and correct the outliers in industrial load curves. First, for the lateral continuity of load data in time sequence, a fuzzy statistical method is employed for classifying the load curves by consumption patterns. The load data sets are classified into two data sets, one is of the basic consumption patterns and the other of special patterns. Then, considering the longitudinal continuity of load values in various time intervals, the nonparametric regression analysis method is used to estimate the center vector based on the data set of basic patterns. With the center vector, the outlier boundaries are achieved to detect all the outliers. Finally, the mapping of load levels is modeled to carry out the outlier correction in accordance with the weighted average method. The actual industrial load data are adopted to test the proposed method. The result shows the effectiveness of the proposed method.
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