A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions

Abstract

The characteristic curve of Photovoltaic (PV) modules is not linear due to environmental conditions. However, there exist only one point in the nonlinear curve of the solar system where the maximum possible power can be extracted. One of the key parts in designing solar systems to increase the output power production is the improvement in Maximum power point tracking (MPPT) methods. Due to fast response and low fluctuations, the adaptive neural-fuzzy inference system (ANFIS) is one of the best methods to find the maximum power point (MPP) in solar systems among various methods. Nevertheless, in proper design of an efficient ANFIS-MPPT, accurate training data is one of the most important challenges. The irradiance and temperature are considered as input variables, while the optimal voltages are output variable optimizing using hybrid whale optimization and pattern search (HWO-PS) algorithm to be used for tuning the incremental conductance (INC). The simulations are performed using Matlab/Simulink to confirm the tracking efficiency of the suggested model. Simulations are performed in different climatic conditions to make the results reliable. The results indicate the proper performance of the proposed method in different climatic conditions with the efficiency of more than 99.3%.