Open AccessFilling Missing Value Method for Power Quality Data Based on Correlation Analysis
Author(s) -
Yundan Liang,
Wei Jiang,
Weiwei Liu,
Shuya Lei,
Wei Lü
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/693/1/012083
Subject(s) - correlation coefficient , correlation , pearson product moment correlation coefficient , data mining , missing data , statistics , regression analysis , power quality , computer science , relation (database) , quality (philosophy) , power (physics) , mathematics , physics , geometry , philosophy , epistemology , quantum mechanics
A large number of monitoring indicators own strong correlation among them which help to better fill missing values in these sensor data. In this study, we propose an electric power quality data filling method based on correlation analysis. Firstly, normalized mutual information method is applied to deal with nonlinear correlation which makes up for the deficiency that the traditional Pearson correlation coefficient. Additionally, the measurement of correlation is calculated to obtain the closely correlated indicators. This study utilizes the regression model to build the strong regression model. Experimental results show that the approach can effectively improve the accuracy of filling, reduce the filling error, and improve the quality of data.