Detection of arc grounding fault based on the features of fault voltage

Arc grounding faults commonly occur in power grids, causing extensive power outages and significant economic losses. In this paper, we propose a novel method for detecting arc grounding faults that utilizes a time-frequency-phase mixed feature extraction approach. The mathematical expression for the arc grounding fault voltage detailed in this paper, and from this, time-domain features that are unaffected by fault transition resistance are extracted. Furthermore, features with clear physical interpretation are extracted from the frequency and phase domains of the fault signal. Our method is able to avoid the subjective feature selection problem that exists current methods. We conduct fault detection experiments using a topology designed for the purpose and a 10 setup to obtain voltage data of arc grounding faults. We then train a fault recognition model using a classic backpropagation neural network. Experimental results demonstrate that the proposed method achieves an ac-curacy of 98.22% in identifying arc grounding faults. Additionally, we apply this method to identify corona discharge and surface discharge, achieving accuracies of 90.68% for corona discharge and 92.9% for surface discharge.