Optimizing grid connected photovoltaic systems using elementary LUO converter and GWO-RBFNN based MPPT

Abstract

The deployment of grid connected photovoltaic (PV) systems has become increasingly vital in the pursuit of sustainable and renewable energy sources. As the global demand for electricity rises, the efficient and reliable incorporation of PV power into electrical grid is of paramount importance. An elementary Luo converter is employed here to enhance the resultant voltage of PV array. To further improve the system’s performance, a Grey Wolf optimized radial basis function neural network (GWO-RBFNN) is employed for maximum power point tracking (MPPT). The GWO algorithm is employed to fine-tune output of RBFNN, making it capable of adaptively extract maximum power. According to the obtained MPP, the input signals to the pulse width modulation generator is tuned using the proposed hybrid MPPT controller. These pulses regulates the operation of elementary Luo converter and guarantees maximum energy conversion efficiency. The converter’s DC link voltage is subsequently subject into grid through a single-phase voltage source inverter which is synchronized with the grid. To facilitate seamless grid integration and synchronization, a conventional proportional integral (PI) controller is deployed. The simulation outputs attained using Matlab results in a robust and efficient system, capable of contributing reliable renewable energy to the grid. The tracking efficiency of the proposed hybrid MPPT controller reaches up to 98.1% and the THD value is reduced to 2.95% which indicates the power quality of the proposed system.