Performance Enhancement of MPPT Controller to Tune Optimal Voltage for PV-BES System Using Converged Barnacles Mating Optimizer Algorithm Based ANFIS

Research into renewable energies is expanding quickly, especially photovoltaic (PV) systems. PV systems are employed extensively in several renewable energy applications. The primary challenge with PV systems is maximizing electricity output. Consequently, a significant amount of research into modeling PV continues to focus on maximizing the generated power. Maximum power point tracking (MPPT) refers to the optimization of PV power generation. Accordingly, an effective MPPT approach deploying a converged barnacles mating optimizer (CBMO)-based adaptive neuro-fuzzy inference system (ANFIS) is introduced in this paper. The mentioned strategy is utilized to detect and track the maximum power point (MPP) in two phases. At the initial stage, ideal voltages are determined using the CBMO algorithm in various temperatures and irradiances in the offline mode. After being trained, the ANFIS calculates the ideal voltage depending on the radiation conditions on solar panels. It then enters the tracking cycle and attempts to identify the MPP. To evaluate the behavior of the suggested technique, a Matlab/Simulink-based MPPT model is created. The proposed approach is evaluated under various weather conditions. The results demonstrate that the suggested methodology for tracking is efficacious under all environmental circumstances. Simulation of the suggested technique is carried out, and the results demonstrate that the introduced MPPT algorithm will effectively give the global maximum under a variety of climatic circumstances. Additionally, this approach is exceedingly effective, rapid, and stable. The findings demonstrate that the suggested technique properly identifies the optimum MPP at 99.3% efficiency.