Enhanced efficiency of a photovoltaic system by the implementation of an adaptive neuro-fuzzy inference system (ANFIS) using MATLAB/SIMULINK to extract maximum powerpoint (MPP)

Solar PV modules have emerged as an important means of power generation, making solar energy a vital resource. The poor efficiency of PV modules, as well as other factors that contribute to low efficiency, such as their sensitivity to changes in solar radiation, temperatures, and climate fluctuations, make the energy they produce ineffective for meeting demand. Therefore, Maximum PowerPoint Tracking (MPPT) technologies play an important role in increasing the production of photovoltaic (PV) modules and improving their efficiency. However, these technologies differ in several things, namely efficiency, tracking speed, accuracy, and complexity. Therefore, it is important to choose a tracking algorithm that has many features to improve the system’s output. Therefore, this article discusses the construction and design process of the neuro-fuzzy inference system (ANFIS) controller, which is highly effective in dealing with non-linear systems. The design consists of a photovoltaic module, an ANFIS reference model, and a DC-DC boost converter. The system also consists of a power controller that controls the operation of the DC-DC converter using fuzzy logic. MATLAB/SIMULINK software was used to simulate and evaluate the system’s performance. The results confirmed the superiority of the strategy in terms of improving system efficiency and reducing the tracking period. In addition, the proposed approach not only ensures fast convergence but also reaches a steady state in the shortest possible time. In addition, the proposed fuzzy logic (FL) power controller for the DC-DC boost converter produced satisfactory results.