Application of neural network modules to electric power system fault section estimation

Summary form only given. This paper presents a neural system intended to aid the control center operator in the task of fault section estimation. Its analysis is based on information about the operation of protection devices and circuit breakers. In order to allow the diagnosis task, the protection system philosophy of busbars, transmission lines and transformers are modeled with the use of two types of neural networks: the general regression neural network (GRNN) and the multilayer perceptron neural network (MLP). The tool described in this paper can be applied to real bulk power systems and is able to deal with topological changes, without having to retrain the neural networks.