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.
Highlights
The presence of harmonics in an electrical system is associated with many problems in its performance
This paper evaluates a new imputation method, which allows the system to fill in the missing data of any of the sensor devices that are used in this research for the recording of voltages, currents and power factors
As was already stated in the section describing the data, due to the random component of both algorithms, a process of MCAR data deletion of 10%, 15%, and 20% of the information was performed five times. The performance of both algorithms was compared by means of root mean square error (RMSE) and mean absolute error (MAE) metrics
Summary
The presence of harmonics in an electrical system is associated with many problems in its performance. As the existence of harmonics cannot be avoided, monitoring in real-time is necessary in order to control them within certain limits. Sometimes they can be transferred by acting on the installation in order to avoid its effects by means of filters either active or passive. In these cases, the use of isolation transformers, super-immunized differential breakers, etc., must be studied
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