Experimental test of optimizing maximum power point tracking performance in solar photovoltaic arrays based on backstepping control and optimized by genetic algorithm

Abstract

Maximum Power Point Tracking (MPPT) is a key component of modern solar photovoltaic (PV) systems. It improves the efficiency of PV power generation systems by inevitably reducing power losses. The most developed MPPT methods adopted like hybrid approaches combine algorithms with nonlinear controllers, such as backstepping controller (BSC). Despite their prevalence, there is still a lack of standardized procedures for tuning the parameters of these nonlinear controllers. In response to this challenge, a proposed method is adopted to identify the optimal gains using Genetic Algorithm (GA) for an efficient power tracking. The advantage of this strategy is established by real-time testing with the GA-BSC technique, which validates its capacity to generate appropriate controller gains for improved tracking performance. Experimental comparisons reveal that the modified GA-BSC approach significantly outperforms the conventional GA-BSC strategy. The conventional GA-BSC method falls short due to its use of non-optimal gains, leading to less effective reference voltage tracking and lower overall performance. In contrast, the proposed method benefits from optimal gains, resulting in enhanced stability and faster response times—achieving maximum power point tracking in 0.2 s compared to 0.6 s for the P&O-BSC method. Another experiment was conducted to test the robustness of the proposed method under variable irradiation profile. The results demonstrated the efficiency of the improved GA-BSC approach compared to other methods, which was more stable and faster. These improvements highlight the capability of the proposed method to enhance hybrid method efficiency, thereby leading to increased efficiency and reliability in photovoltaic systems.