Cost-effective optimization of unified power quality conditioner in wind energy conversion systems using a hybrid EnHBA-GWO algorithm

Abstract

The rapid integration of wind energy conversion systems (WECS) into modern power networks has led to pressing power quality concerns, including voltage instability, harmonic distortion, and reactive power imbalance. To address these challenges, this study introduces a hybrid optimization strategy that combines the global search capabilities of the enhanced honey badger algorithm (EnHBA) with the local exploitation strengths of the grey wolf optimizer (GWO) for the best operational parameters of a unified power quality conditioner (UPQC). Extensive simulations in MATLAB Simulink demonstrate significant improvement in performance. The proposed method achieves 95% energy efficiency, a power factor of 0.99, and total harmonic distortion (THD) down to 5%, meeting IEEE 519-2022 standards. This outcome reflects an effective balance between cost and power quality performance, highlighting the potential of hybrid optimization to improve grid stability and efficiency in renewable energy environments.