Optimization of Fault Current Limiter Reactance Based on Joint Simulation and Penalty Function-Constrained Algorithm DOI Creative Commons
Jun Zhao, Chao Xing, Zhigang Zhang

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1077 - 1077

Published: Feb. 23, 2025

This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB ATP simulation, the accurately derives conditions of objective function, providing critical data support process. To address challenges high computational complexity solution difficulties in constrained optimization, Penalty Function Method (PFM) is employed to transform original problem into standard unconstrained problem, significantly reducing ensuring feasibility solution. On this basis, Gravitational Search Algorithm (GSA) applied compute optimal value. Through comparative analysis engineering case studies, superiority GSA over Genetic (GA) Particle Swarm Optimization (PSO) performance validated, further confirming accuracy efficiency proposed method. The results indicate that not only achieves precise calculation but also improves efficiency. Moreover, integration PFM demonstrates excellent robustness, reliable technical optimized deployment fast-switching limiters large-scale power grids.

Language: Английский

Optimization of Fault Current Limiter Reactance Based on Joint Simulation and Penalty Function-Constrained Algorithm DOI Creative Commons
Jun Zhao, Chao Xing, Zhigang Zhang

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1077 - 1077

Published: Feb. 23, 2025

This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB ATP simulation, the accurately derives conditions of objective function, providing critical data support process. To address challenges high computational complexity solution difficulties in constrained optimization, Penalty Function Method (PFM) is employed to transform original problem into standard unconstrained problem, significantly reducing ensuring feasibility solution. On this basis, Gravitational Search Algorithm (GSA) applied compute optimal value. Through comparative analysis engineering case studies, superiority GSA over Genetic (GA) Particle Swarm Optimization (PSO) performance validated, further confirming accuracy efficiency proposed method. The results indicate that not only achieves precise calculation but also improves efficiency. Moreover, integration PFM demonstrates excellent robustness, reliable technical optimized deployment fast-switching limiters large-scale power grids.

Language: Английский

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