A Fault-Line Selection Method for Small-Current Grounded System Based on Deep Transfer Learning

Abstract

Usually, data-driven methods require many samples and need to train a specific model for each substation instance. As different substation instances have similar fault features, the number of samples required for model training can be significantly reduced if these features are transferred to the substation instances that lack samples. This paper proposes a fault-line selection (FLS) method based on deep transfer learning for small-current grounded systems to solve the problems of unstable training and low FLS accuracy of data-driven methods in small-sample cases. For this purpose, fine-turning and historical averaging techniques are proposed for use in transfer learning to extract similar fault features from other substation instances and transfer these features to target substation instances that lack samples to improve the accuracy and stability of the model. The results show that the proposed method obtains a much higher FLS accuracy than other methods in small-sample cases; it has a strong generalization ability, low misclassification rate, and excellent application value.