Smart Control Solution for Single-Stage Solar PV Systems

Abstract

Solar photovoltaic (PV) systems unpredictable characteristics and tight grid-codes demand power electronic-based energy conversion devices. Hence, as the power levels generated by the solar PV systems rise, multi-level voltage source converters (VSC) and their control mechanisms become more necessary for effective energy conversion. Continuous control set model predictive control (CCS-MPC) is a class of predictive control approach that has emerged recently for the applications of power converters and energy conversion systems. In this paper, an artificial neural network (ANN) based controller for single-stage grid-connected PV is implemented. The CCS-MPC is used as an expert / a teacher to generate the data required for off-line training of the neural network controller. After the off-line training, the trained ANN can fully control the inverter’s output voltage and track the maximum power point (MPP) without the need for MPC during testing. The proposed control technique is assessed under various operating conditions. Based on the results obtained, it is observed that the proposed techniques offer improved objective tracking and comparative dynamic response with respect to the classical approaches.