An Adaptive Neuro-Fuzzy Inference System-based MPPT Controller for Photovoltaic Arrays

This paper presents a Maximum Power Point Tracking (MPPT) method applying an Adaptive Neuro-Fuzzy Inference System (ANFIS) for a stand-alone photovoltaic (PV) system. The proposed ANFIS-based MPPT technique determines the optimal operating point of a PV system which is designed in conjunction with a Z-source DC-DC converter as an interface between the PV array and the load. In the ANFIS-based MPPT controller, real meteorological data are used to define the two input membership function plots assuming that the PV array is located in Ottawa, Canada. The performance of the proposed MPPT technique in tracking the maximum power point (MPP) is assessed numerically in the MATLAB/Simulink environment. The simulation results highlight the benefits of determining reference voltage and duty cycle as output membership functions of the control system without using the current and voltage sensors. Moreover, in comparison with conventional control systems, the proposed solution can reduce the complexity and cost of the control system by eliminating the PID controller.