Optimized Design of LCL Filters for Single-Phase Grid-Connected Inverter Systems using Advanced Optimization Techniques

AI-driven metaheuristic optimization algorithms have demonstrated strong potential in solving complex, high-dimensional engineering design problems. This paper applies and systematically compares three representative swarm-intelligence and evolutionary computation methods—particle swarm optimization (PSO), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)—to the parameter design of LCL filters in single-phase grid-tied inverter systems, a class of problems characterized by nonlinear constraints and competing performance objectives. A structured optimization framework is developed, incorporating an objective function targeting total harmonic distortion (THD) minimization and three engineering constraints governing inductance bounds, damping power loss, and resonance frequency range. Computational and simulation results analyzed under multiple operating conditions—including step-change transients and weak-grid short-circuit faults—demonstrate that PSO achieves superior convergence behavior, lower fitness values, and better robustness compared to WOA and GWO, while ensuring full compliance with IEEE 519 harmonic standards. The findings provide quantitative insights into the comparative computational performance of these algorithms on a constrained continuous optimization problem with direct industrial applicability.