Title :
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
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;
Conference_Titel :
IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
Conference_Location :
Paris
Print_ISBN :
1-4244-0390-1
DOI :
10.1109/IECON.2006.347367
