An Optimized Design Modelling Of A Neural Network Based Green House Management System Using Solar And Rectifying Antenna

The renewable energy resources are widely used in various real time applications, which utilized the solar, wind, fuel cell, etc. From this, the energy management and controlling strategy improves the results. The conventional approach uses Quantum Tunneling PSO for optimization and it is managed with various utility on power grid system. The work utilized the solar and EM waves for energy management scheme and it utilized the controlling parameter by optimization algorithm. The drawback of conventional method is that, the hybrid system utilization and switching is performed with random selection and it not capable for hybrid resources of multiple array functioning. The proposed research work performed with the solar with MPPT tracking and EM with rectenna are utilized and with the help of neural network model, the PV and RF signal generations are stored as array and based on the switching duty cycle from the function of proposed particle swarm optimization, the boost converter act to provide the supply to grid. Through the inverter control, the model fed with the grid, which uses PI controlling with PWM signal generation. Based on the demand and grid utility the LC compensation improves the boost converter performance. The PV and RF signal generation utilized on the continuous utility and obtains the demand free grid circuit. By comparing with the proposed and existing approach, the proposed greenhouse management model obtains the better result. Overall simulink model is done with MATLAB 2018a. Keywords- PV module; EM waves; Rectenna; Proposed PSO; Feed Forward neural network; PI controller and grid utility;