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A Hybrid Algorithm for Missing Data Imputation and Its Application 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 summary is machine-generated.

This study introduces a novel algorithm for imputing missing electrical power network data using Self-Organized Maps and Mahalanobis distances. The new method demonstrates superior performance compared to existing techniques like MICE and AAA.

Keywords:
Adaptive Assignation Algorithm (AAA)Mahalanobis distancesMultivariate Adaptive Regression Splines (MARS)Self-Organized Maps Neural Networks (SOM)currentmissing data imputationmultivariate imputation by chained equations (MICE)power factorquality of electric supplyvoltage

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Area of Science:

  • Electrical Power Systems Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Accurate data is crucial for analyzing electrical power networks, including harmonic detection and phase imbalance.
  • Missing data in key electrical variables (voltage, current, power factor) negatively impacts time series analysis.

Purpose of the Study:

  • To develop and evaluate a new missing data imputation algorithm for electrical power network data.
  • To compare the proposed algorithm against established methods like Multivariate Imputation by Chained Equations (MICE) and the Adaptive Assignation Algorithm (AAA).

Main Methods:

  • Development of a novel imputation algorithm utilizing Self-Organized Maps Neural Networks and Mahalanobis distances.
  • Comparative analysis with Multivariate Imputation by Chained Equations (MICE).
  • Comparative analysis with the Adaptive Assignation Algorithm (AAA).

Main Results:

  • The proposed algorithm effectively imputes missing electrical data.
  • The new method demonstrates superior performance over both MICE and AAA.
  • Validation of the algorithm's efficacy in handling missing data in power system studies.

Conclusions:

  • The novel Self-Organized Maps and Mahalanobis distance-based algorithm is a highly effective solution for missing data imputation in electrical power networks.
  • This method offers significant improvements over existing imputation techniques.
  • The proposed algorithm addresses a critical challenge in power system data analysis.