ANN Based Single Phase Bidirectional DC-AC Boost Inverter for Grid Connected Solar Photovoltaic Systems without a Transformer

The paper describes an artificial neural network (ANN)-based single-phase bidirectional DC-AC boost inverter for grid-connected solar PV systems without a transformer. The proposed inverter architecture effectively converts PV panel-generated DC power into AC power that can be injected into the grid. The typical boost inverter isolates the PV array from the grid with a large, costly transformer. The suggested technology is ideal for grid-connected PV systems since the inverter is smaller, cheaper, and simpler without a transformer. An ANN trained on simulated input-output data controls the bidirectional inverter. The ANN can accurately predict the grid voltage and frequency, as well as the duty cycle required for maximum power point tracking (MPPT). Using a realistic solar PV system model, comprehensive simulations were done to test the suggested inverter. Results show that the ANN-based control technique tracks the maximum power point well and stabilizes grid integration without a transformer. The suggested inverter has high efficiency, minimal THD, and quick grid disturbance response. Bidirectional power flow lets the PV system and grid exchange energy. The ANN-based single-phase bidirectional DC-AC boost inverter for grid-connected solar PV systems outperforms transformer-based inverters and costs less. The suggested design might help the grid embrace solar PV systems for clean, sustainable energy.