A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers.
The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms.
Maintenance Department, University of Oviedo, San Francisco 3, Oviedo 33007, Spain. ccrespo@uniovi.es.
Concepción Crespo
CC
Turrado
Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, Gijón 33204, Spain. sanchezfernando@uniovi.es.
Fernando
F
Sánchez Lasheras
Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain. jlcalvo@udc.es.
José Luis
JL
Calvo-Rollé
Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain. andres.pinon@udc.es.
Andrés-José
AJ
Piñón-Pazos
Electrical Engineering Department, University of Oviedo, Campus de Viesques, Gijón 33204, Spain. melero@uniovi.es.
Manuel G
MG
Melero
Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo 33004, Spain. fjcos@uniovi.es.
Francisco Javier
FJ
de Cos Juez
Adaptive Assignation Algorithm (AAA); Mahalanobis distances; Multivariate Adaptive Regression Splines (MARS); Self-Organized Maps Neural Networks (SOM); current; missing data imputation; multivariate imputation by chained equations (MICE); power factor; quality of electric supply; voltage