METAHEURISTIC OPTIMISATION OF SHUNT ACTIVE POWER FILTER CONTROL FOR POWER QUALITY ENHANCEMENT IN HYBRID RENEWABLE MICROGRIDS

The growing global demand for electricity, driven by rapid industrialisation and population growth, has intensified the need for sustainable and high-quality power systems. Hybrid renewable energy source (HRES)–based microgrids provide a practical pathway for integrating clean energy; however, the widespread use of nonlinear loads and power electronic interfaces introduces persistent power quality challenges, including harmonic distortion, voltage sags, and unbalanced fault conditions. In this study, an intelligent optimisation-based control strategy for a shunt active power filter (SAPF) is proposed using the Mountain Gazelle Optimiser (MGO), a recently developed bio-inspired metaheuristic algorithm, to tune proportional–integral (PI) controller gains dynamically. The MGO-optimised PI controller adaptively regulates inverter switching signals to enhance harmonic mitigation, reactive power compensation, and overall system stability. A detailed MATLAB/Simulink model of a grid-connected hybrid photovoltaic–fuel cell–battery microgrid is developed to assess the proposed approach under severe power quality disturbances, including voltage sag events and line-to-line-to-ground faults. Simulation results demonstrate that the MGO-based SAPF achieves substantial performance improvements over conventional PI control, including a 42.53% reduction in total harmonic distortion under fault conditions, an 8.08% improvement during voltage sag events, an 80.87% reduction in voltage deviation, and a 3.45% enhancement in power factor, while maintaining compliance with IEEE 519 standards. These results highlight the effectiveness of the proposed MGO-PI control framework as a robust, adaptive power-conditioning solution for enhancing power quality in renewable-rich microgrids and supporting the operational requirements of future intelligent energy systems.