Simulation of Superconductive Fault Current Limiter (SFCL) Using Modular Neural Networks
Paper in proceedings, 2006

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

Fault current limiters

Power engineering computing

Hopfield neural nets

Superconducting devices


Behrooz Makki

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

Nasser Sadati

Mohammad Sohani

32nd IEEE Annual Conference on Industrial Electronics

1553-572X (ISSN)

Vol. 1 1 4415-4419

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering



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