A novel photovoltaic array outlier cleaning algorithm based on moving standard deviation

被引：0
|
作者：
Shi M. [1 ]
Yin R. [1 ]
Hu A. [1 ]
Wu J. [3 ]
机构：
[1] State Grid Hebei Electric Power Supply Co., Ltd., Shijiazhuang
[2] North China Electric Power University, Beijing
[3] China Electric Power Research Institute, Nanjing
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 06期
基金：
中国国家自然科学基金;
关键词：
cleaning algorithm; data cleaning; outlier; power curve; PV array;
D O I：
10.19783/j.cnki.pspc.190484
中图分类号：
学科分类号：
摘要：
There are a large number of outliers in the PV array operation data. The outlier will bring difficulties to the functions such as PV array performance analysis, modeling, and fault diagnosis. In order to effectively clean the outlier in the PV array operation data, this paper proposes a cleaning method for PV array outlier based on moving standard deviation. It analyzes the source and distribution characteristics of the array outlier data and proposes the algorithm based on moving standard deviation. The curve’s rising of the sliding standard deviation set is used as the basis for judging the outlier data. Finally, through the case analysis and comparison of quartile method, the results show that the algorithm can effectively reduce the cleaning error caused by the concentration distribution of the outlier. © 2020 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码：108 / 114
页数：6
相关论文
共 23 条
  • [1] National power industry statistics for 2018
  • [2] KAMENOPOULOS S N, TSOUTSOS T., Assessment of the safe operation and maintenance of photovoltaic systems, Energy, 93, pp. 1633-1638, (2015)
  • [3] GE Shuangye, YANG Lingfan, LIU Qian, Et al., Research on photovoltaic MPPT simulation under dynamic shadow environment based on modified CPSO, Power System Protection and Control, 47, 6, pp. 151-157, (2019)
  • [4] CHEN J, LI W, LAU A, Et al., Automated load curve data cleansing in power systems, IEEE Transactions on Smart Grid, 1, 2, pp. 213-221, (2010)
  • [5] WANG Y, INFIELD D G, STEPHEN B, Et al., Copula-based model for wind turbine power curve outlier rejection, Wind Energy, 17, 11, pp. 1677-1688, (2015)
  • [6] SHEN X, FU X, ZHOU C., A combined algorithm for cleaning abnormal data of wind turbine power curve based on change point grouping algorithm and quartile algorithm, IEEE Transactions on Sustainable Energy, 10, 1, pp. 46-54, (2019)
  • [7] ZHAO Y, LIN Y E, WANG W, Et al., Data-driven correction approach to refine power curve of wind farm under wind curtailment, IEEE Transactions on Sustainable Energy, 9, 1, pp. 95-105, (2018)
  • [8] GONG Yingfei, LU Zongxiang, QIAO Ying, Et al., Copula theory based machine identification algorithm of high proportion of outliers in photovoltaic power data, Automation of Electric Power Systems, 40, 9, pp. 16-22, (2016)
  • [9] DUAN Simo, MIAO Shihong, HUO Xuesong, Et al., Modeling and dynamic correlation analysis of wind/solar power joint output based on dynamic Copula, Power System Protection and Control, 47, 5, pp. 35-42, (2019)
  • [10] YE X, LU Z, QIAO Y, Et al., Identification and correction of outliers in wind farm time series power data, IEEE Transactions on Power Systems, 31, 6, pp. 4197-4205, (2016)
123