A transformer differential protection based on finite impulse response artificial neural network

This paper presents the application of a finite impulse response artificial neural network (FIRANN) on digital differential protection design for a three-phase transformer. The neural network inputs are normalized sampled current data. Any pre-processing signal as in other neural network applications is not needed. The network was trained to identify external fault on load side besides internal fault as in the other differential protection. The FIRANN has 6 inputs and 2 outputs. The first output goes on when there is an internal fault while the second output goes on in case of external fault. The simulated system used to get data for training and testing the neural network is presented. The neural network architecture and some of the obtained results are reported.