A real-time hardware fault detector using an artificial neural network fordistance protection

A real-time fault detector for the distance protection application, based o n artificial neural networks, is described. Previous researchers in this fi eld report use of complex filters and artificial neural networks with large structure or long training times. An optimum neural network structure with a short training time is presented, Hardware implementation of the neural network is addressed with a view to improve the performance in terms of spe ed of operation. By having a smaller network structure the hardware complex ity of implementation reduces considerably, Two preprocessors are described for the distance protection application which enhance the training perform ance of the artificial neural network many folds. The preprocessors also en able real-time functioning of the artificial neural network for the distanc e protection application. Design of an object oriented software simulator, which was developed to identify the hardware complexity of implementation, and the results of the analysis are discussed, The hardware implementation aspects of the preprocessors and of the neural network are briefly discusse d.