Weak Single Phase-to-Ground Fault Time Detection With Learnable-Parameter-Driven DSP-Enhanced Transformer

Huan Luo, Qiyue Li, Hao Zhou, Wei Sun, Weitao Li, Zhi Liu, Yusheng Ji, Lijian Ding

Abstract: Accurate location of Single Phase-to-Ground Faults (SPGFs) in Resonantly Grounded Networks is crucial for timely fault clearing, requiring precise dual-time detection of fault initiation and duration. However, existing methods struggle to address the weak and nonlinear characteristics of fault currents. To tackle these challenges, we propose a paradigm that integrates neural networks with an enhanced Digital Signal Processing (DSP) method. This approach replaces traditional feature extraction with DSP-based methods and dynamically optimizes DSP parameters through network feedback. Specifically, we introduce a transient weak feature-enhanced attention mechanism and a learnable nonlinear waveform reconstruction mechanism, culminating in a parameter-driven DSP-enhanced Transformer named 2TDformer. Furthermore, we construct a dual-time SPGF detection dataset and publicly release it to facilitate model evaluation and support broader research. Experimental results demonstrate that our method achieves maximum errors of less than 1.4 ms for fault initiation detection and 2.8 ms for duration detection. Compared to existing fault detection methods and the state-of-the-art time series anomaly detection models, 2TDformer reduces detection errors by 30%, setting a new benchmark in performance.

External IDs:dblp:journals/tsg/LuoLZSLLJD26