PROJECT TITLE :

Artificial Neural Network for Control and Grid Integration of Residential Solar Photovoltaic Systems - 2017

ABSTRACT:

Residential solar photovoltaic (PV) energy is changing into an increasingly vital part of the globe's renewable energy. A residential solar PV array is typically connected to the distribution grid through one-phase inverter. Management of the single-phase PV system should maximize the power output from the PV array whereas ensuring overall system performance, safety, reliability, and controllability for interface with the electricity grid. This paper has two main objectives. The first objective is to develop a synthetic neural network (ANN) vector management strategy for an LCL-filter based mostly single-section solar inverter. The ANN controller is trained to implement optimal management, based on approximate dynamic programming. The second objective is to evaluate the performance of the ANN-based mostly solar PV system by simulating the PV system behavior for grid integration and maximum power extraction from solar PV array in a realistic residential PV application and building an experimental solar PV system for hardware validation. The results demonstrate that a residential PV system using the ANN control outperforms the PV system using the traditional commonplace vector management method and proportional resonant control technique in both simulation and hardware implementation. This can be additionally true within the presence of noise, disturbance, distortion, and nonideal conditions.