Hybrid SOSMC-GA based control strategy for performance optimization of grid-connected PV systems

Abstract

• Development of a hybrid Second Order Sliding Mode Controller (SOSMC). • Optimization of SOSMC parameters using Genetic Algorithms (GA). • Developed SOSMC-GA method reduces current THD and chattering effects of classical SMC. • Validation of SOSMC-GA's robustness in face of changes in radiation and temperature. • Validation of SOSMC-GA’s robustness in the case of partial shading and a grid fault. The global energy transition requires the development of robust Artificial Intelligence (AI) control solutions to optimize the integration of a Photovoltaic (PV) systems into the power grid. In this context, this study proposes the design and optimization of a nonlinear controller based on a hybrid strategy combining Second Order Sliding Mode Control (SOSMC) and Genetic Algorithms (GA) in order to improve the performance of a 100 kW PV system subjected to different irradiation, temperature, and partial shading conditions, as well as sudden grid disturbances. Power conversion is achieved through two stages, and the performance of the proposed control scheme is evaluated using Matlab/Simulink. The simulation results highlight the superiority of SOSMC-GA, which reduces overshoot by 98 % compared to a conventional Current Vector Controller (CVC). Under standard conditions, the Total Harmonic Distortion (THD) of the current is reduced to 0.63 %, compared to 1.09 % for SOSMC and 2.19 % for CVC. In addition, the system response time is significantly reduced, confirming the effectiveness of the proposed controller for optimizing grid-connected PV systems.