Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques.

To maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer's internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short‐time Fourier transform of the signal. Both developed models are light and have the minimum number of layers that can achieve a very high performance. The performance of the proposed models is tested using a dataset prepared according to laboratory‐measured transformer parameters. The results are compared with other well‐known methods, and the achievement of high numerical performance evaluation validates the consistency of the proposed methods. [ABSTRACT FROM AUTHOR]