Next Article in Journal
Disposable Non-Enzymatic Glucose Sensors Using Screen-Printed Nickel/Carbon Composites on Indium Tin Oxide Electrodes
Previous Article in Journal
Compressive Sensing Based Design of Sparse Tripole Arrays
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(12), 31069-31082; doi:10.3390/s151229842

A New Missing Data Imputation Algorithm Applied 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
Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo 33004, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 7 October 2015 / Revised: 7 December 2015 / Accepted: 7 December 2015 / Published: 10 December 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [216 KB, uploaded 10 December 2015]

Abstract

Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm. View Full-Text
Keywords: missing data imputation; multivariate imputation by chained equations (MICE); Multivariate adaptive regression splines (MARS); quality of electric supply; voltage; current; power factor missing data imputation; multivariate imputation by chained equations (MICE); Multivariate adaptive regression splines (MARS); quality of electric supply; voltage; current; power factor
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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Crespo Turrado, C.; Sánchez Lasheras, F.; Calvo-Rollé, J.L.; Piñón-Pazos, A.J.; de Cos Juez, F.J. A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers. Sensors 2015, 15, 31069-31082.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top