Adaptive neuro-fuzzy approach for maximum power point tracking with high gain converter for photo voltaic applications

Abstract

Energy is essential for improving the management of the power systems. One of the major concerns in the power sector is increasing demand of power that tends to increase the demand for fossil fuels causing environmental problems. Thus, it is essential to use variable renewable energies for direct current (DC) micro grid applications. Solar photo voltaic (PV) is one of the phenomena where the solar irradiance is converted to electrical power through solar cell. A new high gain converter is implemented which is fed from solar PV to track the maximum voltage and power so as to elevate the output efficiency of the PV panel. The envisioned converter can be effectively worked in continuous conduction mode (CCM) with same gating pulse for the two switches that guarantees wide plying range. The preferred converter comprises least number of components and achieves a maximum gain of 25 for the switch on period of 0.8 which makes the control circuit simpler compared to other non-coupled inductor topologies. Presently, special maximum power point tracking (MPPT) procedures have been acquainted to track the maximum power point (MPP) viably of which adaptive neuro fuzzy inference system (ANFIS) based controller is used in this article and high efficiency of about 98.96% is attained with good dynamic response. Over here, the potency of the system is endorsed by means of MATLAB/SIMULINK and compared with the other standard MPPT methods. Further, functioning and assessment of the conspired work are inspected carefully and verified effectively.