Neural network-based quickprop control algorithm for grid connected solar PV-DSTATCOM system

For the optimal operation of grid interfaced solar photovoltaic (PV) system, a neural network-based Quickprop control algorithm is presented in this study. The solar PV array supplies maximum power by utilising an incremental conductance-based maximum power point tracking technique to the grid and the load. When the solar power is not present, during cloudy days or at night, the distribution static compensator (DSTATCOM) operation is performed by harmonics mitigation and reactive power compensation of the loads connected at the point of intersection. The proposed system improves power quality when solar PV power is present, along with active power transfer from solar PV array to grid/load. Thus, a smooth transition is provided between these modes with neural network-based Quickprop control algorithm. Moreover, the neural network-based control technique offers enhanced accuracy due to the combinational neural structure in the estimation process. A laboratory prototype is developed for validation and experimental results corroborate reliable operation for modes of operation as DSTATCOM and grid interfaced PV system at varying load and solar insolation condition.