Investigating the Potential of an ANFIS-Based Maximum Power Point Tracking Controller for Solar Photovoltaic Systems

This paper proposes, the design and modelling of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller. The proposed system is designed to maximize the efficiency of PV modules under varying environmental conditions and offers a dynamic and adaptive control strategy to accommodate changes in temperature, irradiance, and load. The implementation of this strategy entails the definition of fuzzy rules, which are delineated in accordance with the operating conditions of the PV modules. These rules provide a logical structure that enables the system to make accurate and rapid decisions by using the input parameters of temperature, irradiance, and load to reach the maximum power point. The employment of fuzzy logic is instrumental in accommodating the intricate and non-linear characteristics inherent in the system, thereby facilitating the dynamic attainment of the optimal operating point in accordance with the prevailing environmental conditions. The efficacy of the proposed ANFIS-based MPPT controller is evaluated through extensive simulations conducted in the MATLAB/SIMULINK environment. The simulation results demonstrate the effectiveness of the system in operating under various load, temperature, and irradiance conditions. In addition, comparisons with the conventional Incremental Conductance (INC) MPPT technique indicate that the ANFIS-based controller provides a more stable and faster dynamic response, reducing oscillations around the maximum power point (MPP).

Keywords: PV systems, MPPT methods, DC-DC converter, adaptive neuro-fuzzy inference, system (ANFIS), increased conductivity (INC), fuzzy logic controller (FLC).