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Modeling superconductive fault current limiter using constructive neural networks

Makki, B ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ISIE.2007.4375066
  3. Publisher: 2007
  4. Abstract:
  5. Although so many advances have been proposed in the field of artificial intelligence and superconductivity, there are few reports on their combination. On the other hand, because of the nonlinear and multivariable characteristics of the superconductive elements and capabilities of neural networks in this field, it seems useful to apply the neural networks to model and control the superconductive phenomena or devices. In this paper, a new constructive neural network (CNN) trained by two different optimization algorithms; back-propagation and genetic algorithm, is proposed which models the behavior of the superconductive fault current limiters (SFCLs). Simulation results show that the proposed approach is in good harmony with the real characteristics of the SFCLs. ©2007 IEEE
  6. Keywords:
  7. Artificial intelligence ; Critical current density (superconductivity) ; Critical currents ; Diesel engines ; Electric fault currents ; Electronics industry ; Genetic algorithms ; Industrial electronics ; Limiters ; Penetration depth (superconductivity) ; Technical presentations ; Vegetation ; Back-propagation ; Constructive neural networks ; Fault current limiter ; Superconductive fault current limiters (SFCLs) ; International symposium ; Multi variables ; Optimization algorithms ; Simulation results ; Neural networks
  8. Source: 2007 IEEE International Symposium on Industrial Electronics, ISIE 2007, Caixanova - Vigo, 4 June 2007 through 7 June 2007 ; 2007 , Pages 2859-2863 ; 1424407559 (ISBN); 9781424407552 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4375066