Performance Assessment of Classification Methods for the Inductance within a VSI

The non-intrusive monitoring of electrical systems has gained relevance, during the last years, due to its lower costs and space requirements. Machine learning techniques have proved their ability to predict the parameters under monitoring and consequently improve the performance of power electronics systems. The present work seeks to determine the combination of machine learning techniques and dimensionality reduction that efficiently predicts the inductance value for a Voltage Source Inverter's Modulate Model Predictive Control (VSI_M2PC). The problem, modeled as a classification one with three classes, has a high dimensionality 5000 attributes. Consequently, its reduction is needed to make it tractable at the cost of slightly sacrificing the accuracy of the model. Seven machine learning methods Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Linear Discriminant Analysis, Classification and Regression Trees, C4.5 and Random Forest. Additionally, the strategies for dimensions reduction Correlation Elimination, Principal Component Analysis, and Boruta were experimentally studied on VSI_M2PC Matlab simulations. It was found that Random Forest combined with the Boruta provided the best results regarding classification efficiency.