Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms

Abstract

To achieve an accurate, efficient, and high dynamic control performance of electric motor drives, precise phase voltage information is required. However, measuring the phase voltages of electrical motor drives online is expensive and potentially contains measurement errors, so they are estimated by inverter models. In this paper, the idea is to investigate if various machine learning (ML) algorithms could be used to estimate the mean phase voltages and duty cycles of the black-box inverter model and black-box inverter compensation scheme with high accuracy using a publicly available dataset. Initially, nine ML algorithms were trained and tested using default parameters. Then, the randomized hyper-parameter search was developed and implemented alongside a 5-fold cross-validation procedure on each ML algorithm to find the hyper-parameters that will achieve high estimation accuracy on both the training and testing part of a dataset. Based on obtained estimation accuracies, the eight ML algorithms from all nine were chosen and used to build the stacking ensemble. The best mean estimation accuracy values achieved with stacking ensemble in the black-box inverter model are R¯2=0.9998, MAE¯=1.03, and RMSE¯=1.54, and in the case of the black-box inverter compensation scheme R¯2=0.9991, MAE¯=0.0042, and RMSE¯=0.0063, respectively.