Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control

The main purpose of this paper to compare and analyze three types of controllers in the three phases DC-AC inverters in hybrid renewable energy source (HRES) systems. To achieve this, two modern controllers are developed and compared based on sliding mode control (SMC) and artificial neural network techniques. The HRESs comprise photovoltaic (PV), wind turbines, battery storage systems, and transmission lines connected to infinite bus bars via a step-up transformer. The developed controllers at the inverter side utilize both voltage control and current regulation. A DC-DC boost converter is employed to set up a voltage demand at the point of common coupling (PCC). Then, the formulation of an HRES with the developed controllers is presented. The developed controllers are considered to operate under various solar radiations, temperatures, and wind speed loading conditions. The HRESs with the developed controllers are simulated via MATLAB/Simulink to verify the effectiveness of the developed controllers. The obtained results demonstrate that adaptive SMC and artificial ANN control techniques give better results in terms of input power, output power, current, and voltage when compared to classic PI control.