Optimization of Fault Current Limiter Reactance Based on Joint Simulation and Penalty Function-Constrained Algorithm

Jun Zhao (), Chao Xing, Zhigang Zhang, Boyuan Liang, Lu Sun, Bin Wei, Weiqi Qin and Shuguo Gao
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Jun Zhao: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
Chao Xing: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
Zhigang Zhang: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
Boyuan Liang: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
Lu Sun: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
Bin Wei: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Weiqi Qin: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Shuguo Gao: Electric Power Research Institute, State Grid Hebei Electric Power Company, Shijiazhuang 050000, China

Energies, 2025, vol. 18, issue 5, 1-16

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

Keywords: fault current limiter; optimal configuration of reactance value; joint simulation; penalty function method; gravitational search algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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