Speaker
Description
The increasing penetration of renewable energy sources (solar and wind energy) into modern power grids introduces significant power quality challenges, notably power fluctuations due to their intermittent nature and total harmonic distortion (THD) caused by power electronic-based interface devices, such as grid-connected inverters. Addressing these issues requires robust control strategies to enhance system stability and efficiency. This study implements a proportional-integral (PI) current control strategy optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with Ziegler-Nichols (Z-N) tuning serving as a benchmark. The performance of the control strategy is evaluated using the Integral Time Absolute Error (ITAE) metric. Simulation results demonstrate that PSO yields superior control accuracy, reducing ITAE to 0.1605 for the D-axis and 0.1491 for the Q-axis control, followed by GA with 0.2053 and 0.1519, while Z-N records significantly higher values of 1.7658 and 1.6595, respectively. In terms of power quality, the total voltage harmonic distortion (THD_V) remains at 0.0% due to effective filtering, while the total current harmonic distortion (THD_i) is minimized to 0.17% with PSO, compared to 0.21% for GA and 1.98% for ZN. The optimized PI control strategy ensures that THD values comply with IEEE 519-2014 and IEC 61000-3-6 Standards, demonstrating its effectiveness in mitigating power fluctuations, reducing harmonics, and improving grid stability. These findings emphasize the critical role of intelligent optimization in enhancing power quality for grid-connected renewable energy systems.
