CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

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

Behrooz Makki (Institutionen för signaler och system, Kommunikationssystem) ; Nasser Sadati ; Mohammad Sohani
32nd IEEE Annual Conference on Industrial Electronics (1553-572X). Vol. 1 (2006), 1, p. 4415-4419.
[Konferensbidrag, refereegranskat]

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

Nyckelord: Hopfield neural nets; Fault current limiters; Power engineering computing; Superconducting devices

Denna post skapades 2009-02-26. Senast ändrad 2017-10-03.
CPL Pubid: 90440


Läs direkt!

Länk till annan sajt (kan kräva inloggning)

Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem (1900-2017)


Elektroteknik och elektronik

Chalmers infrastruktur