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 a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W 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%.
Highlights
The sun is an inexhaustible source of renewable energy, generating little or no waste or polluting emissions
In a PV system, the Maximum Power Point Tracking (MPPT) command can be defined as an algorithm which associated with an adaptation stage allows the system to operate in its optimal operating point and this whatever the atmospheric conditions and of load value [1]
The neuro-fuzzy MPPT control algorithm is associated to the converter to operate the generator at its maximum power point
Summary
The sun is an inexhaustible source of renewable energy, generating little or no waste or polluting emissions. Photovoltaic solar energy is the transformation of part of the light from solar irradiation into electrical energy using a set of elements constituting a PV system whose basic phenomenon implemented is the photovoltaic effect This form of energy has the advantage of stabilizing global warming, preserving our fossil fuel reserves and ensuring energy security for the planet. The maximum power point (MPP) changes position with the change of light intensity or temperature, and Pascal Kuate Nkounhawa et al.: MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions the optimal voltage changes value. Fuzzy logic controller (FLC) and artificial neural networks (ANN) have been used successfully to track the peak power point of PV systems [2, 3].
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