Adapted multilayer feedforward ANN based power management control of solar photovoltaic and wind integrated power system

This paper proposes a DC linked hybrid solar photovoltaic/wind energy system for stand-alone applications. Solar and wind energy are utilized as primary energy sources and battery unit is considered as storage to meet the primary load demand. Loads are considered according to their priority such as primary, deferrable and dump loads. Among these loads primary load is having highest priority. Ratings of hybrid energy system components such as solar PV, wind generator, battery unit, power electronic converter is optimally selected for the given load profile. Adapted multilayer feed forward artificial neural network structure is utilized to compute the maximum power developed by nonlinear and intermittent nature of solar and wind energy sources. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources, the storage unit and loads in the system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink. The system performance under different scenarios has been verified by carrying out simulation studies using a practical load demand profile and real weather data.