Optimization Design of a Saturated Iron Core Fault Current Limiter Using a GA and PSO Algorithms Coupled With Finite Element Method

A DC-bias high-temperature superconducting (HTS) coil is the main part of the Saturated Iron Core Fault Current Limiter (SIC-SFCL). Another relevant part of the SIC-SFCL is the iron core, which represents the main volume of the device. Therefore, optimizing the iron core volume decreases the equipme...

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Bibliographic Details
Published in:IEEE transactions on applied superconductivity 2023-03, Vol.33 (2), p.1-12
Main Authors: Santos, Gabriel dos, Sass, Felipe, Hugo, Vitor, Sotelo, Guilherme, Vilhena, Nuno, Oliveira, Roberto, Pronto, Anabela, Pina, Joao Murta
Format: Article
Language:English
Subjects:
Algorithms
Coated Conductor
Coils
Current limiters
Design optimization
Design parameters
Equipment costs
Fault Current Limiter
Ferromagnetic materials
Finite element method
Genetic Algorithm
Genetic algorithms
High temperature
Iron
Magnetic cores
Optimization
Particle Swarm Optimization
Programming languages
Saturation magnetization
Short circuits
Software
Superconducting coils
Superconducting magnets
Superconducting tapes
Superconductivity
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Summary:A DC-bias high-temperature superconducting (HTS) coil is the main part of the Saturated Iron Core Fault Current Limiter (SIC-SFCL). Another relevant part of the SIC-SFCL is the iron core, which represents the main volume of the device. Therefore, optimizing the iron core volume decreases the equipment cost significantly. Also, it is important to find an optimized geometry for the DC HTS coil that allows extracting the maximum potential of the superconducting material. This paper proposes a full design optimization of a SIC-SFCL, combining the genetic algorithm (GA) and the particle swarm optimization (PSO) methods. For the optimization procedure, the authors consider the superconducting tape and the ferromagnetic material. The COMSOL software is used to implement the finite element model and the Python language coupled to the COMSOL software codes the optimization algorithms. The authors study different cognitive, social, and inertial parameters in PSO, in order to evaluate the impact of those parameters in the optimization. The results present the ability of the algorithm to provide adequate SIC-SFCL design parameters without any expert intervention. To validate the proposed methodology, the authors perform experimental tests and compare them with the simulation results. The steady-state regime presents an error less than 1% and the short-circuit regime shows a maximum error equal to 10.3%.
ISSN:1051-8223
1558-2515
DOI:10.1109/TASC.2022.3222260