LUO CONVERTER ALONG WITH NEURAL NETWORK BASED ADAPTIVE DROOP CONTROLLER DECOUPLER FOR A GRID TIED PV SYSTEM

Droop control is a pivotal aspect of the operation and control of distributed generation systems like PV systems as it coordinates their operation to ensure the reliable and efficient working of the entire system.Thereby, a novel Neural Network Decoupler based droop control approach is presented in this work to effectively address load changes, disturbance and voltage problems in a grid tied PV system.The inclusion of Neural Network Decoupler, improves the adaptability, stability and dynamic response of droop control along with the facilitation of optimal grid voltage synchronization.Additionally, it is also a cost effective and simple technique due to the absence expensive hardware or complex control algorithm.In case of PV system, a new Improved Luo converter in addition to Recurrent Neural Network (RNN) based Maximum Power Point Tracking (MPPT) approach is used to achieve required utility level voltage from the PV system.The potential of presented droop controller is justified through MATLAB simulation and laboratory prototype implementation.From studying the obtained results, it is evident that the suggested approach is effective in maintaining a stable grid voltage despite the variable voltage from the PV system and the instability caused by the BLDC motor load.Moreover, the efficiency of the Improved Luo converter is also estimated to be around 98.6%.