Abstract
The aim of this paper is to present a new approach to fault area estimation for high-speed relaying using feedforward neural networks. The suggested framework makes use of neurocomputing technology and 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 nonlinear functions and their large input error tolerance. The obtained results indicate that these characteristics still result in reliable behaviour even if nonideal (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.
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