Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Abstract

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge due to the weak fault current and complicated working conditions. In this paper, we present a deep learning-based multi-label classification framework to reliably distinguish the faulty feeder. Three different neural networks (NNs) including the multilayer perceptron, one-dimensional convolutional neural network (1D CNN), and 2D CNN are built. However, the labeled data may be difficult to obtain in the actual environment. We use the simplified simulation model based on a full-scale test field (FSTF) to obtain sufficient labeled source data. Being different from most learning-based methods, assuming that the distribution of source domain and target domain is identical, we propose a samples-based transfer learning method to improve the domain adaptation by using samples in the source domain with proper weights. The TrAdaBoost algorithm is adopted to update the weights of each sample. The recorded data obtained in the FSTF are utilized to test the domain adaptability. According to our validation and testing, the validation accuracies are high when there is sufficient labeled data for training the proposed NNs. The proposed 2D CNN has the best domain adaptability. The TrAdaBoost algorithm can help the NNs to train an efficient classifier that has better domain adaptation. It has been therefore concluded that the proposed method, especially the 2D CNN, is suitable for actual distribution networks.