Intelligent energy management in renewable-powered microgrid using Z-dualflux converter and RBFNN control

In this research, a sophisticated intelligent energy management framework that integrates wind, solar and battery energy storage devices is proposed for a grid-connected system. A Z-Dual Flux (Z-DF) converter which enhances the Photovoltaic (PV) system’s voltage with increased voltage gain, decreased magnetic stress and greater conversion efficiency. To attain voltage regulation and dynamic response of Z-DF converter in diverse solar settings, a hybrid Bird Swarm–Graylag Goose (BS–GG) optimization-PI controller is employed. To facilitate variable-speed operation and effective power extraction, the wind system employs a Doubly Fed Induction Generator (DFIG) system. A Radial Basis Function Neural Network (RBFNN) controller is employed as an intelligent supervisory controller to handle system nonlinearities, load fluctuations and parameter uncertainties. A Three-phase Voltage Source Inverter ( \\(\\:3\\phi\\:\\) VSI) with an LC filter is employed to satisfy grid power quality necessities. Battery discharging and charging are controlled by a bidirectional converter, which also serves as an energy buffer when there is a power imbalance. The attainment of the developed control architecture is validated by outcomes in the MATLAB tool that reveals the converter efficiency of 94.9%, lowermost THD of 0.09% and the BS-GGO-PI controller attains the settling time of 0.15 s with improved DC-link voltage stability, decreased total harmonic distortion and increased overall energy efficiency.