A power transformer protection with recurrent ANN saturation correction

Current transformers (CTs) are present in electric power systems for protection and measurement purposes and they are susceptible to the saturation phenomenon. This paper presents an alternative approach to the correction of distorted waveforms caused by CT saturation. The method uses recurrent artificial neural networks (ANN) algorithms. As an example of an application, a complete protection system for a power transformer based on the deferential logic has been utilized. The EMTP-ATP software has been chosen as the computational tool to simulate the electrical system in order to generate data to train and test the ANNs. Many ANN architectures were trained and tested. Encouraging results related to the application of the new method are presented.