REAL-TIME AND OFF-LINE TRANSMISSION-LINE FAULT CLASSIFICATION USING NEURAL NETWORKS

This paper is concerned with application of Neural Networks (NNs) to f ault classification for both the real-time applications such as protec tive relaying of transmission lines and the off-line applications such as post-mortem study of fault events recorded with Digital Fault Reco rders (DFRs). A supervised learning NN of the same type is utilized fo r both applications. It has been demonstrated that the NN approach rea ches performance of the existing techniques in both application areas and yet shows some additional benefits.