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A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers.

Concepción Crespo Turrado1, Fernando Sánchez Lasheras2, José Luis Calvo-Rollé3

  • 1Maintenance Department, University of Oviedo, San Francisco 3, Oviedo 33007, Spain. ccrespo@uniovi.es.

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Summary

This study introduces a new method for filling in missing electrical data in power grids, outperforming existing techniques. This improves time series analysis for harmonics and phase imbalances.

Keywords:
Multivariate adaptive regression splines (MARS)currentmissing data imputationmultivariate imputation by chained equations (MICE)power factorquality of electric supplyvoltage

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Area of Science:

  • Electrical Engineering
  • Data Science

Background:

  • Data collection is crucial for analyzing electrical power networks, particularly for identifying harmonics and phase imbalances.
  • Missing data in key electrical variables (voltages, currents, power factor) hinders time series analysis.
  • Data imputation is essential to replace missing values with reliable estimates.

Purpose of the Study:

  • To present a novel data imputation method for electrical power networks.
  • To compare the proposed method with the established Multivariate Imputation by Chained Equations (MICE) technique.
  • To evaluate the performance of the new method in handling missing electrical data.

Main Methods:

  • Development of a missing data imputation method utilizing Multivariate Adaptive Regression Splines (MARS).
  • Comparative analysis of the MARS-based method against the MICE algorithm.
  • Evaluation using key electrical variables such as phase-to-neutral voltage, phase-to-phase voltage, current, and power factor.

Main Results:

  • The proposed MARS-based imputation method demonstrated superior performance compared to MICE.
  • The novel method effectively substitutes missing electrical data, enhancing time series study reliability.
  • MARS shows significant advantages in accurately estimating missing values in power system data.

Conclusions:

  • The MARS-based approach is a highly effective technique for missing data imputation in electrical power networks.
  • This method offers an improved alternative to MICE for handling incomplete datasets in power system analysis.
  • Accurate data imputation is vital for robust studies on power quality and network stability.