Hybrid MPPT-based optimised double-stage controller for grid-integrated photovoltaic system

Integrating photovoltaic (PV) systems with grid networks has grown in response to the rising demand for renewable energy sources. This study tackles the requirement for effective integration of PV systems with grid networks, emphasising a two-stage PV-connected grid system. The primary goal is to maximise PV array power extraction while maintaining flawless grid synchronisation. A significant gap in current approaches has been observed, notably in Maximum Power Point Tracking (MPPT) and grid synchronisation. To overcome this, a new hybrid MPPT approach is suggested, which combines incremental conductance and Neural Network (NN) techniques. This hybrid technique uses both algorithms’ precision and speed to measure maximum power output from PV panels under various environmental circumstances. Furthermore, the study addresses grid synchronisation issues by suggesting a PID controller optimised using Genetic Algorithm (GA) to govern power flow between the PV system and the grid. The GA technique improves transient responsiveness and lowers steady-state errors by optimising the PID controller’s parameters. The efficacy of the suggested method is confirmed by simulation results, which show that the GA-PID controller outperformed conventional PID controllers in terms of settling time improvement (by 33.3%), overshoot reduction (by 84.0%), and \({V}_{\text{RMSE}}\) reduction (up to 28.6%). With \({P}_{\text{RMSE}}\) reductions of up to 85.3%, the GA-PID controller also demonstrated a notable improvement in synchronisation accuracy and resilience under dynamic grid settings.