A Deep Learning based Energy Management System for Solar-Wind Hybrid Energy System

Energy is incredibly important in our daily lives since it is essential to meeting our necessities. Since there are numerous energy sources, we always choose to use environmentally friendly renewable energy to protect the environment. Given that one of the primary sources of renewable energy is solar energy. This paper utilizes a photovoltaic (PV) power-generation system with a sophisticated DC-DC Landsman Converter. By using INC MPPT, the architecture reduces switching losses and increases the efficiency of the PV power conditioning systems. The suggested Deep learning based ANN controller design makes use of PWM approach, which controls the output of DC-DC converter. By turning on both active power switches at zero voltage, this converter can lower switching losses and clearly increase conversion efficiency. A single-phase inverter receives the output of the DC-DC Landsman converter, which keeps the output sinusoidal with minimal harmonic distortion, and injects it into the PCC. The final system has a very high tracking speed, low cost, and excellent efficiency. MATLAB Simulink will be used for the circuit simulation and hardware is fabricated to test the controller performance.