Simulation of Superconductive Fault Current Limiter (SFCL) Using Modular Neural Networks

Author

Makki, Behrooz ; Sadati, Nasser ; Sohani, Mohammad

Author_Institution

Dept. of Biomed. Eng., Amirkabir Univ. of Technol., Tehran

fYear

2006

fDate

6-10 Nov. 2006

Firstpage

4415

Lastpage

4419

Abstract

Modular neural networks have had significant success in a wide range of applications because of their superiority over single non-modular ones in terms of proper data representation, feasibility of hardware implementation and faster learning. This paper presents a constructive multilayer neural network (CMNN) in conjunction with a Hopfield model using a new cost function to simulate the behavior of superconductive fault current limiters (SFCLs). The results show that the proposed approach can efficiently simulate the behavior of SFCLs

Keywords

Hopfield neural nets; fault current limiters; power engineering computing; superconducting devices; Hopfield model; constructive multilayer neural network; cost function; data representation; modular neural networks; superconductive fault current limiter; Biological neural networks; Circuits; Cost function; Fault current limiters; Fault currents; Hopfield neural networks; Immune system; Multi-layer neural network; Neural networks; Superconductivity;

fLanguage

English

Publisher

ieee

Conference_Titel

IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on

Conference_Location

Paris

ISSN

1553-572X

Print_ISBN

1-4244-0390-1

Type

conf

DOI

10.1109/IECON.2006.347367

Filename

4153244