Modeling Superconductive Fault Current Limiter using Constructive Neural Networks
Paper in proceedings, 2007

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.

Constructive neural networks

SFCL

Backpropagation

Artificial intelligence

Genetic algorithm

Author

Behrooz Makki

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Nasser Sadati

Mona Noori-Hosseini

IEEE symposium on Industrial Electronics-ISIE

Vol. 1 1 2859-2863

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-1-4244-0755-2

More information

Latest update

8/7/2018 1