Multineural network based fault area estimation for high speed protective relaying

The aim of this paper is to present a new approach to fault area estimation for high-speed relaying using feed-forward neural networks. The suggested framework makes use of neurocomputing technology an pattern-recognition concepts. In contrast to conventional algorithms, our neural fault area estimator (NFAE) determines the fault area directly. This approach leads to very short propagation times and reliable classification results. Important attributes of artificial neural networks (ANNs) are their ability to learn non-linear functions [8] and their large input error tolerance. The obtained results indicate that these characteristics still result in reliable behaviour even if non-ideal (real-world) effects pertain. A comparison of classification quality with conventional algorithms by simulating certain faults on a parallel transmission line shows the approaches advanced capability for protective relaying.