Dynamic Bidirectional-Controlled Inverter-Based Grid Optimized by Neural Networks

Grid-based voltage source inverters frequently utilize the droop control technique combined with inner/outer voltage and current regulation mechanisms to ensure a dependable electricity supply. This study seeks to introduce a Cascade-Forward Neural Network (CFNN) control approach designed to lead inverter-based grids when operating in grid-connected or islanded modes, focusing on improving the transient state performance of the CFNN technique. The suggested approach involves utilizing the inverter in a bidirectional manner, suitable for a diverse array of battery energy storage systems and distributed generation setups. The proposed strategy leverages CFNN to grasp the inverter’s non-linear model, enabling precise monitoring of demand and reference power across various operational scenarios within smart grid applications. Furthermore, the approach redefines the grid control concept, guiding the inverter according to optimal parameters that encompass power demand, reference power, equipment dimensions, and external disturbances. Notably, this method circumvents the need for any manual tuning procedures. Furthermore, incorporating dynamic elements into the approach improves the protection system’s responsiveness. This ensures continuous power supply during faults, enabling a more effective and rapid response, enhancing system resilience, and reducing downtime. This dual advantage of better power supply and heightened protection system sensitivity highlights the proposed method’s significance in fortifying the reliability of power systems.. To assess the efficacy of the suggested CFNN controller, its power tracking, operational capabilities, and dynamic response are assessed via multiple experimental trials employing the hardware-in-the-loop (HIL) approach across various scenarios. The outcomes of these tests are meticulously compared to a well-established conventional strategy, affirming the effectiveness of the proposed method.