Adaptive Cascaded ANFIS MPPT Development for Solar and Fuel Cell Based Hybrid Energy System

The concept of DC microgrids has gained immense popularity in recent times due to excessive application of direct current (DC) sources including Fuel cells and Photovoltaic (PV) system, along with several DC loads and energy storage devices such as batteries. In this research work, a DC microgrid connected Hybrid energy system that consists of PV in addition with Fuel cell and a battery is examined. The interfacing of the Fuel cell and PV system to DC microgrid is done separately through two independent unidirectional DC–DC Boost converter, whereas a Bidirectional Buck Boost converter (BBBC) is chosen for interfacing battery to the DC microgrid. In the PV system, Cascaded Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method is adopted to optimize the functioning of the Boost converter to enable maximum extraction of solar energy. To ascertain the efficiency of the proposed DC microgrid, simulations are performed using Matlab software. The proposed hybrid energy system is effective in delivering improved voltage stability. The cascaded ANFIS method is compared with conventional MPPT techniques and the attained outcomes show that Cascaded ANFIS has an outstanding efficiency of 91%.