Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation

Abstract

By increasing the photovoltaic (PV) systems capacity worldwide, the requirement for a fast, reliable, and efficient control system is becoming more crucial. To this end, model predictive control (MPC) is known as one of the potential solutions. Although MPC is an easily implemented control system, it needs a high computational complexity due to the dependency on solving an iterative optimization problem. To overcome this problem, this study develops an artificial intelligence-based on one-dimensional convolutional neural network (1D-CNN) based MPCs. While 1D-CNN benefits from the inherent strong feature extraction/selection capability and lower computational complexity than other deep methods, it still cannot properly track the dynamic changes due to fixed weights during the training process. Thus, this paper integrates the dynamic weighting training process and proposed dynamic weighing 1D-CNN for the implementation of intelligent MPC for the PVs. The numerical results based on different load types show an efficient performance of the proposed system and verify the superiority of the proposed method in comparison with the conventional MPC and several state-of-the-arts shallow and deep based MPC for the PVs in terms of the total harmonic distortion (THD) and frequency switching.