A novel global MPPT technique to enhance maximum power from PV systems under variable atmospheric conditions

The study of renewable energy is expanding quickly, particularly in the areas of modeling and photovoltaic (PV) technology. The utilization of photovoltaic systems is prevalent in diverse renewable energy applications. The primary concern of photovoltaic systems is the optimization of output power to achieve maximum efficiency. Consequently, numerous investigations are being conducted to model photovoltaic systems with the aim of enhancing the power output. The process of maximizing power output on photovoltaic systems is commonly referred to as maximum power point tracking (MPPT). The implementation of MPPT methods is a crucial aspect of photovoltaic system engineering, aimed at enhancing the overall output power of photovoltaic panels. The adaptive neural-fuzzy inference system (ANFIS) has been identified as the most efficient approach for MPPT due to its rapid response time and reduced oscillation, despite the existence of alternative techniques. Nevertheless, the acquisition of precise training data poses a significant obstacle in the development of an effective ANFIS-MPPT. This study focuses on the utilization of the modified fluid search optimization (MFSO) algorithm to regulate the incremental conductance (INC) controller, thereby ensuring maximum power tracking. The input variables considered in this investigation are the irradiance as well as temperature, while the output parameter is the optimum voltage ( V mpp ). A model for MPPT is constructed using MATLAB/Simulink in order to evaluate the performance of the proposed approach. The method being proposed has been subjected to testing across various weather conditions. The outcomes of the simulation demonstrate the proficient monitoring of the suggested approach in the presence of various environmental circumstances. The study employs a simulation-based approach to evaluate the efficacy of the MFSO-ANFIS-based MPPT algorithm in achieving global maxima under diverse climate conditions. The obtained results validate the proposed method's effectiveness. This approach exhibits a high degree of efficiency, speed, and stability. The findings indicate that the suggested approach effectively monitors the improved maximum power point with a performance rate exceeding 99.3%.