Fault Line Selection Method of Small Current Grounding System Based on Deep Learning

Abstract

At present, the single-phase grounding fault line selection problem of small current grounding system has not been completely solved. In order to improve the success rate of single-phase grounding fault line selection, a new method based on deep learning network was proposed. Firstly, the simulation model of neutral un-grounded system was built by PSCAD. By setting the fault of each line under different grounding resistors, the sample data based on the zero sequence current value and phase angle of each line was obtained out. The sample data was divided into three parts:training set, verification set, and test set. Secondly, a deep learning neural network was constructed based on Keras, and the network was trained using the training set and validation set. Finally, the trained model was tested The results show that the method has the characteristics of simple modeling, high success rate and no influence of transition resistance.

Keywords

• small current grounding system
• singlephase grounding fault
• fault line selection
• deep learning