Design and Implementation of Model Parameter Independent Robust Current Control Scheme of Three-Phase Inverter - A Neural Network-Based Classification Approach

In recent years, there has been a notable surge of interest in integrating advanced control techniques within power electronic systems. This article presents the utilization of neural network (NN) controllers within the realm of three-phase inverter control. While traditional control methods like proportional integral-derivative (PID) and pulse width modulation (PWM) have proven effective, they sometimes fall short of meeting the demands of modern applications. These contemporary requirements encompass heightened precision, adaptability to changing conditions, and resilience against uncertainties. This study employs an NN controller to achieve current control in a three-phase standalone inverter system. A dataset is prepared using model predictive control (MPC) to train the neural network model, and appropriate hyperparameters are chosen, facilitating offline learning. The entire setup is implemented within the MATLAB Simulink platform, allowing for an in-depth analysis of its performance. This analysis includes the assessment of prediction errors and the evaluation of total harmonic distortion (THD). In addition, the article conducts a comparative study between the neural network controller and the MPC controller, presenting and discussing the obtained results. Further, the proposed method is realized in the hardware in loop OPAL - RT setup, and the real-time performance is analyzed.