Voltage SAG/SWELL Emimination Using UPQC PSO ANN

The efficiency of an electrical system heavily relies on power quality, making the integration of Unified Power Quality Conditioners (UPQCs) with Distributed Generation (DG) systems vital for mitigating issues like flicker, harmonics, voltage sags, and swells. In UPQC-DG systems, Proportional-Integral (PI) controllers are crucial for maintaining DC link voltage stability, ensuring consistent power delivery and smooth grid interaction. Properly tuned PI controllers reduce voltage ripples and enable rapid response to disturbances, thus enhancing overall system performance. However, traditional tuning methods like Ziegler-Nichols often fall short in dynamic and complex conditions.This paper introduces a novel real-time PI controller optimization method using Particle Swarm Optimization (PSO) combined with Artificial Neural Networks (ANN). This hybrid PSO-ANN approach adaptively adjusts PI parameters in real time, ensuring precise DC link voltage control across varying operating conditions. By integrating PSO’s global search capabilities with ANN’s predictive learning, the system achieves enhanced adaptability and robustness. The proposed method demonstrates significant improvements in power quality, reliability, and stability for UPQC-DG systems.