Sensorless Adaptive control of VSI-Fed Induction Motor Drive with Optimized MLP-Neural Network

Abstract

ABSTRACT Multilayer-Perceptron Neural Network (MLP-NN)-based sensorless speed control of adaptive Indirect Field-Oriented Control (IFOC) strategy is implemented for online parameter estimation of Induction Motor Drive (IMD) fed from Common mode voltage injection Space vector PWM (CVSVPWM) based Voltage Source Inverter. Harris Hawks Optimization (HHO) is implemented in this work, to train the MLP-NN model by choosing the optimal weight and biases for the estimation of accurate parameters and speed of IMD. The objective of optimal MLP-NN is to improve the IMD reliability and response fast during dynamic operation. The model performances are evaluated by employing statistical metrics of MSE, RMSE, MAE, MAPE, and R for training and testing. These are reported for testing to be 0.000602064, 0.0245, 0.4015, 0.25474, and 0.9997 which indicates the best-fitted prediction model and proves the minimized error. The results reveal that an optimized MLP-NN accomplishes promising performance in estimating the parameters and speed with the least errors such as rs is 3.82%, rr is 4.19%, ls is 0.41%, lr is 0.72%, lm is 0.21%, and strongly tracking of reference speed. In addition, HHO is also employed to evolve the gains of the PI-controller in adaptive-IFOC for generation of reference signals by reducing the computational effort.