Analysis and Design of Reliable Converter Topology for Grid Connected PV Systems with ANN Controller

This research paper introduces a comprehensive analysis and design of a dependable converter topology specifically designed for grid-connected photovoltaic (PV) systems. The main objective of this study is to optimize power output and enhance the reliability of the system by presenting a modified version of the cascaded H-bridge inverter (CHB) that incorporates an artificial neural network (ANN) controller. The performance of the proposed topology is compared against two commonly used converter topologies, namely the two-level inverter and the neutral point clamped (NPC) inverter. Through extensive simulations, it is evident that the proposed topology, along with the ANN controller, yields notable improvements in power quality, efficiency, and reliability when compared to the alternative topologies. It demonstrates exceptional capability in handling grid disturbances and maintaining a stable power output even under varying operating conditions. The integration of the ANN controller empowers the converter topology to acquire learning abilities and adaptability to ensure stable and reliable operation despite changes in solar power inputs and atmospheric conditions. The outcomes of this research contribute significantly to the progress of PV system integration into the grid, offering a promising solution for grid-connected PV systems. The proposed topology, accompanied by the ANN controller, exhibits superior performance and reliability, rendering it suitable for a wide range of operating conditions. The study underscores the significance of developing converter topologies capable of effectively managing fluctuations in PV system output and ensuring consistent power supply quality, thereby facilitating the successful integration of renewable energy sources into the grid.