Performance analysis of automated QFT robust controller for long-term grid tied PV simulations

Abstract

Long-term simulations are significant to understand the real-time operation of grid-tied renewable energy system configurations. Grid-tied photovoltaic system (GPV) is highly non-linear due to the dependency of real-time meteorological conditions. The non-linear behavior of the photovoltaic (PV) system with the power electronic converter makes the long-term simulation inefficient and slow. This paper presents an efficient and simple modelling approach for GPV modelling suitable for long-term simulations. The recent advancements in control strategies and system configurations, sub-module level controller operation gained much interest but the simulation of such systems can be very challenging due to a large number of power electronic components and their control, non-linear behavior of PV system. This paper proposed a genetic algorithm based robust controller design in the quantitative feedback theory (QFT) framework to extract the maximum power from GPV at the sub-module level to extradict the power losses due to partial shading conditions. The performance of the proposed controller at the PV sub-module level is evaluated through comparison with the Q-parameterization based controller. The proposed QFT methodology based robust controller is shown to have advantages over Q-parameterization approach to simulate long-term GPV operation. © 2020 IEEE.