Meta-heuristic-based power quality improvement in UPQC-based grid-connected hybrid renewable energy system

The integration of renewable energy sources (RES) into the power grid has become an increasingly important aspect of modern power systems. However, the intermittent and variable nature of RES presents challenges for maintaining power quality (PQ) in the grid. One solution to address these challenges is the use of a Unified Power Quality Conditioner (UPQC) in a grid-connected hybrid renewable energy system (HY-RES). This paper proposes an approach to address power quality (PQ) issues in a hybrid renewable energy system (HY-RES) that incorporates a grid-connected load. The proposed approach utilizes a Unified Power Quality Conditioner (UPQC) device to minimize PQ disturbances and meet load demand in the system. To improve the performance of the UPQC, an optimized PI controller is introduced. To enhance the performance of the PI Controller, the gain parameters (Proportional gain \({k}_{p}\) $k_p$ and integral gain \({k}_{i}\) $k_i$ ) are fine-tuned using the new hybrid optimization algorithm called Particle Updated Greywolf Optimizer (PUGO). The proposed Particle Updated Greywolf Optimizer (PUGO) model is the combination of the standard Particle Swarm optimization (PSO) and Grey wolf optimization (GWO), respectively. The proposed method is evaluated using MATLAB/Simulink, and its performance is compared to existing techniques such as GWO and PSO algorithms. The results show that the proposed approach outperforms existing techniques in mitigating PQ issues such as sag, swell, fluctuation, and Total Harmonic Distortion (THD). Overall, the proposed UPQC-based PC-GWO approach offers an effective solution for improving PQ in HY-RES systems with grid-connected loads.