Performance Comparisons of Bio-Inspired Optimization Algorithms for Grid Synchronization

This paper evaluates the performance of three optimization algorithms, Particle Swarm Optimization (PSO), Gray Wolf Optimization with Ant Colony Optimization (GOA-ACO), and Gray Wolf Optimization with Whale Optimization Algorithm (GOA-WOA)—for grid synchronization tasks. The algorithms were tested on three critical objectives: voltage synchronization error minimization, total harmonic distortion (THD) reduction, and transient response optimization. The results demonstrate that PSO excels in steady-state optimization tasks such as voltage synchronization error and THD reduction due to its rapid convergence. However, its performance in dynamic scenarios, such as transient response optimization, is limited. GOA-ACO, while slower in convergence, balances exploration and exploitation effectively, making it suitable for complex solution spaces. GOA-WOA outperforms the other algorithms in dynamic optimization tasks, achieving the best results for transient response optimization with minimal recovery time and oscillations. GOA-WOA reached a fitness value of 0.1695 for transient response in 5 steps, doing better than PSO (0.0947) and GOA-ACO (0.1187). For THD reduction, GOA-ACO demonstrated a balanced approach, converging to a THD value of 0.1187 after 20 iterations. The step response comparison further highlights GOA-WOA's superior dynamic performance, with faster settling time and minimal overshoot. These findings suggest that while PSO is ideal for steady-state tasks requiring quick solutions, GOA-WOA is better suited for applications demanding robust fault recovery and adaptability. This paper provides valuable insights into the selection of optimization algorithms for grid synchronization in renewable energy systems.