Simulation Based Estimation of GMPP Using ANFIS Technique for Photovoltaic System Under Varying Weather Conditions

Existing AI-based Maximum Power Point Tracking (MPPT) scheme use sensory information such as solar irradiance, solar power system input voltage, and input current measurements to forecast and estimate the GMPP throughout the non-linear P-V curve, thereby overcoming the constraints of traditional MPPT methods. Several meteorological and environmental factors can affect the power output of solar PV modules. Solar irradiance and cell temperature under different environmental circumstances are the vital meteorological parameters that directly affect the MPPs of a solar power system. This work outlined the basic concept of maximum power point tracking and compare some performing AI-based MPPT algorithms for GMPP estimation. After assessing and comparing these approaches, we proposed a Modified Genetic Algorithm (GA) trained Adaptive Neuro-Fuzzy Inference System (ANFIS). This method was modelled and simulated in Simulink to demonstrate the method’s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values.