Stator single-line-to-ground fault protection for powerformers based on HSGC and CNN

Abstract

It is necessary for powerformers running in parallel to identify which powerformer occurs at the stator single-line-to-ground (SLG) fault. Some state-of-the-art fusion discriminations are used to identify stator SLG fault, but these methods extract fault features artificially, and application conditions are limited. Convolutional neural network (CNN) has shown superior automatic feature extraction ability in various fields, but it cannot directly extract features from one-dimensional time series vectors collected by powerformers. Therefore, this article proposed a novel SLG fault protection scheme based on the hub-and-spoke grid data converting algorithm (HSGC) and CNN. First, Pearson product-moment correlation coefficients (PCCs) are used to calculate the correlations of one-dimensional time series vectors, establish a correspondence between them and the distance of two-dimensional grid cells, and then convert one-dimensional time series vectors to two-dimensional grid-structured data by HSGC. Second, the trained CNN automatically extracts the features of two-dimensional grid-structured data. Finally, the faulty powerformer can be identified based on the output of CNN. The proposed protection scheme is verified through the simulation of ATP-EMTP and Python. The results show that the scheme can accurately detect a faulty Powerformer under different conditions where neutral point is high-resistance or reactance grounding, even if fault resistance is 8,000 Ω.