‏Real-time validation of advanced control strategies for grid-connected PV-wind hybrid systems using adaptive...

Sophisticated optimization and management techniques are now needed to ensure optimal power harvesting, improved power quality, and reliable grid synchronization due to the rapidly growing demand for renewable systems like wind and photovoltaic (PV) under highly variable environmental conditions. The integration of these resources into smart power grids presents critical challenges related to stability, power quality, and energy extraction efficiency. When applied to a grid-connected hybrid PV-Wind energy system, the Adaptive Starfish Optimization Algorithm (ASFOA), the Greedy Man Optimization Algorithm (GMOA) and the Particle Swarm Optimization Algorithm (PSO) are thoroughly compared in this study with an emphasis on two key optimization layers: Maximum Power Point Tracking (MPPT) and PI controller tuning for inverter control. The MATLAB/Simulink model and simulation of the system include PV arrays, a PMSG-based wind turbine, DC-DC converters, and a PWM-controlled inverter connected to the grid via a stabilized DC-link. The simulation results show that the two approaches perform very differently. GMOA and PSO demonstrated faster convergence, stronger global search capability, and fewer steady-state oscillations, all of which improved DC-link voltage stability and dynamic response under abrupt changes in solar irradiance and wind speed. In contrast, ASFOA achieved higher power accuracy, lower overshoot, and smoother transient response, resulting in reduced power ripples and stronger local optima exploitation. Comparative graphical analysis includes power response curves, convergence profiles, DC-link stability, inverter current waveforms. Comparative graphical analysis including power response curves, convergence profiles, DC-link stability, inverter current waveforms, and Total Harmonic Distortion (THD) spectrum evaluation confirmed the superiority of the optimized controllers over conventional control methods. The system achieved a significant reduction in THD, leading to improved power quality and robust grid synchronization even during abrupt disturbances. This paper is among the first to investigate and validate the combined use of GMOA, PSO and ASFOA in grid-connected hybrid renewable systems, demonstrating that the integration of GMOA for global search and ASFOA for fine local optimization forms a powerful and reliable intelligent control strategy. The proposed approach significantly enhances energy harvesting, power quality, and system resilience, making it a promising solution for real-time energy management and next-generation smart grid applications. Furthermore, the OPAL-RT real-time implementation confirms the robustness and practical feasibility of the proposed ASFOA-based control strategy. The strong agreement between simulation and real-time results demonstrates its effectiveness for real PV systems, providing improved dynamic response and enhanced power extraction under varying environmental conditions.