Recurrent Neural Networks Based Differential Protection of Power Transformers

Document Type : Original Article


Electrical Engineering Dept., Faculty of Eng., Mansoura University


                Power transformers are important electrical equipments that need fast protection, because of their essential role in power system operation and their expensive cost. The most common technique used to protect the transformer is the differential relay, but it doesn't provide discrimination between internal fault and inrush currents. This paper presents an algorithm based on recurrent neural network (RNN) as a differential protection for three phase two windings transformer. The algorithm uses both the primary and secondary currents and second order harmonics of currents to discriminate between internal fault and inrush currents. A comparison among the performance of three neural networks based classifiers is presented. These networks are: FFBPNN (feed forward back propagation), cascade-forward back propagation network (CFBPNN), and proposed recurrent network (RNN). The transformer fault conditions are simulated using PSCAD/EMTDC in order to obtain the primary and secondary current signals. These current signals are used to train and test the neural networks which implemented by Matlab/Simulink. The test results prove that the RNN is stable and give good behaviors for different fault conditions. It is more reliable for recognition of transformer inrush and internal fault currents.