design of three-phase grid-connected pv system with z-source boost converter and neural network-based harmonic elimination

The progress towards renewable energy sources (ress) has highlighted the crucial role of photovoltaic (pv) systems in meeting the energy demands of the world.  as energy from pv system becomes significant, there is need for efficient and high quality grid systems becomes essential. as a consequence, this research proposes a grid connected pv system to address the challenges by integrating z-source boost converter (zsbc) and neural network based controller for eliminating harmonics. the zsbc is employed to overcome the limitations of conventional dc-dc converters, offering greater flexibility in voltage boosting and improved reliability in power conversion. additionally, the system utilizes a cascaded adaptive neuro-fuzzy inference system (anfis) based maximum power point tracking (mppt) controller to ensure that the pv array operates at its maximum efficiency under all conditions, thereby maximizing energy harvest. to continue high power quality and ensure grid compliance, the system incorporates a dq based artificial neural network (ann) controller. the system validation is carried out using matlab, with results demonstrating superior performance in terms of 96.42% converter efficiency and 1.23% total hormonic distortion (thd). this innovative approach not only improves the overall efficiency and reliability but also ensures that it meets the requirements of modern grids.