A DC-DC Converter Topology for Optimal Utilization of Hybrid Renewable Energy Resources Using Hybrid Technique

This article proposes a DC-DC converter for the optimal use of hybrid renewable energy sources (HRES) with the hybrid control approach using the proposed technology. HRES-like photovoltaic (PV), wind turbine (WT), and battery energy storage technology are examined (BESS). The proposed hybrid control technique is a hybrid wrapper of the Marine Predators Algorithm (MPA) and recalling-enhanced recurrent neural network (RERNN). The biological interaction of MPA is improved using the crossover (C) and mutation (M) method, which is later referred to as the combined CM-MPA (C2MPA) technique. The proposed system is called as C2MPA-RERNN technique. The C2MPA approach is used in the proposed method to evaluate the exact control signals system and to build up a control signals database for offline use according to power variation of source and load sides. The collected dataset is utilized to run the RERNN technique online, allowing the control method to run in less time. Based on the error value of active and reactive power, the C2MPA-RERNN determines optimal gain parameters of a proportional integral (PI) controller. The real and reactive power values are input algorithms. The system error is diminished, and optimal power flow management is connected to tune the PI parameters. The proposed system is tested using the MATLAB/Simulink platform. The gain parameters like K-p and K-i of the proposed system refer 2.5 and 150, respectively.