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.
Highlights
The importance of problems due to harmonics in electric networks is growing
The performances of the three algorithms were compared based on the mean absolute error (MAE) and root mean square error (RMSE) metrics
As can be observed in this table, for the variables of voltage, intensity and power factor employed in this research, the RMSE values obtained by the new algorithm are considerably lower than those obtained by using the Assignation Algorithm (AAA) and Multivariate Imputation by Chained Equations (MICE) methods
Summary
The importance of problems due to harmonics in electric networks is growing. This fact is due to the increase in the amount of non-linear loads. The monitoring of harmonics in real time is required to control them. Another common problem in electrical networks is the imbalance between phases. This is usually caused by a bad load distribution between phases and provokes a high current return displayed by the neutral, as it has to compensate for the gap existing at the centre of the scheme vectors
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