MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions

Abstract

Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop an MPPT controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar type FL-M-160W photovoltaic module submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.