TY  - JOUR
AU  - Hussain, Imad
AU  - He, Yigang
AU  - Khan, Atta Ullah
AU  - Ali, Yaqoob
AU  - Khan, Idrees
PY  - 2026
DA  - 2026/04/05
TI  - Optimized Design of LCL Filters for Single-Phase Grid-Connected Inverter Systems using Advanced Optimization Techniques
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 2
IS  - 3
SP  - 158
EP  - 172
DO  - 10.62762/TACS.2025.424683
UR  - https://www.icck.org/article/abs/TACS.2025.424683
KW  - swarm intelligence
KW  - evolutionary computation
KW  - AI-driven  optimization
KW  - metaheuristic algorithms
KW  - constrained parameter optimization
KW  - LCL filter design
KW  - weak grid
AB  - 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.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Hussain2026Optimized,
  author = {Imad Hussain and Yigang He and Atta Ullah Khan and Yaqoob Ali and Idrees Khan},
  title = {Optimized Design of LCL Filters for Single-Phase Grid-Connected Inverter Systems using Advanced Optimization Techniques},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {158-172},
  doi = {10.62762/TACS.2025.424683},
  url = {https://www.icck.org/article/abs/TACS.2025.424683},
  abstract = {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.},
  keywords = {swarm intelligence, evolutionary computation, AI-driven  optimization, metaheuristic algorithms, constrained parameter optimization, LCL filter design, weak grid},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}