Artificial Neural Network Grid-Connected MPPT-Based Techniques for Hybrid PV-WIND with Battery Energy Storage System
Tóm tắt
A hybrid photovoltaic–wind–battery–microgrid system is designed and implemented based on an artificial neural network with maximum power point tracking. The proposed method uses the Levenberg–Marquardt approach to train data for the ANN to extract the maximum power under different environmental and load conditions. The control strategies adjust the duty cycle of a DC boost converter to achieve maximum power output for photovoltaic and wind energy systems. DC bus voltage implements voltage control using a bidirectional converter with a battery, and the current flow is controlled by an inverter control for the grid-side converter based on the state of charge of the battery and the photovoltaic current received. A phase-locked loop for frequency and phase synchronization of the grid and an LCL filter to eliminate harmonics in the single-phase grid are also integrated into the microgrid system.
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