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Sensors 2016, 16(9), 1467; doi:10.3390/s16091467

A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers

1
Maintenance Department, University of Oviedo, San Francisco 3, Oviedo 33007, Spain
2
Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, Gijón 33204, Spain
3
Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain
4
Electrical Engineering Department, University of Oviedo, Campus de Viesques, Gijón 33204, Spain
5
Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo 33004, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Kemal Akkaya
Received: 13 July 2016 / Revised: 5 September 2016 / Accepted: 7 September 2016 / Published: 10 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [541 KB, uploaded 10 September 2016]   |  

Abstract

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. View Full-Text
Keywords: missing data imputation; multivariate imputation by chained equations (MICE); Mahalanobis distances; Self-Organized Maps Neural Networks (SOM); Adaptive Assignation Algorithm (AAA); Multivariate Adaptive Regression Splines (MARS); quality of electric supply; voltage; current; power factor missing data imputation; multivariate imputation by chained equations (MICE); Mahalanobis distances; Self-Organized Maps Neural Networks (SOM); Adaptive Assignation Algorithm (AAA); Multivariate Adaptive Regression Splines (MARS); quality of electric supply; voltage; current; power factor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Turrado, C.C.; Sánchez Lasheras, F.; Calvo-Rollé, J.L.; Piñón-Pazos, A.-J.; Melero, M.G.; de Cos Juez, F.J. A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers. Sensors 2016, 16, 1467.

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