Hybrid Active Power Filter (HAPF) performance is strongly affected by the nonlinear behavior and tight coupling of control parameters, which makes traditional optimization techniques prone to unstable tuning and unreliable performance when applied to fractional-order controllers. This paper proposes an advanced control framework for HAPFs based on a novel hybrid meta-heuristic optimization approach. The method combines the adaptive search capability of the Pelican Optimization Algorithm (POA) with the social intelligence of the Grey Wolf Optimizer (GWO) to achieve a more balanced and reliable tuning process than standalone methods to efficiently tune all five parameters of a Fractional Order PID (FOPID) controller. The objective is to improve dynamic stability and harmonic attenuation under diverse operating conditions. Simulations carried out in the MATLAB/Simulink (R2018a) environment demonstrate that the proposed hybrid POA-GWO approach outperforms conventional PID controllers and FOPID controllers optimized using single algorithms. Key improvements include significant reduction in total harmonic distortion (THD) where THD of source current reduces from 28.95% to 4.34%, also the proposed hybrid FOPID controller demonstrates faster convergence and achieves a lower objective function value compared to individual optimization algorithms and conventional controllers, The results also demonstrate enhanced durability under balanced and unbalanced loading conditions. The results confirm the effectiveness of the proposed controller as a practical solution for real-time power quality enhancement in emerging smart grid applications.
Eldeen et al. (Wed,) studied this question.