High-Impedance Fault Detection Method Based on One-Dimensional Variational Prototyping-Encoder for Distribution Networks

Designing a neural network using simulation waveforms for high-impedance fault (HIF) detection is a challenging task, particularly in case of applications on distribution networks. Based on the characteristics of HIF transient zero sequence currents (TZSCs), a novel methodology is proposed for HIF detection in the time domain, which includes 1-D variational prototyping-encoder (1-D-VPE) and decision tree (DT) algorithm. The proposed HIF detection scheme consists of four steps: First, the TZSCs collected from simulations are applied to train the 1-D-VPE neural network, whose encoder is used as the feature extractor. Then, the extracted features are utilized to train the DT classifier. After that, the well-trained encoder and DT are employed to test field TZSCs. Finally, the 1-D-VPE neural network is fine-tuned with some of the field TZSCs. The performance of the proposed method is analyzed using waveform samples collected from the PSCAD/EMTDC simulation platform and the actual distribution networks. The results show that the proposed method provides good classification performances on both simulation and field data at high noise levels without transformation between different domains, and the classification accuracy can be improved by fine-tuning with some field TZSCs.