Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network

IEEE Access

Fengyi Guo¹  Yanli Liu²  Ying Li²  Wenchu Li² 

[1] College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China;Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, China;

关键词: Series arc fault;    deep learning;    recurrent neural network;    RNN;    fault diagnosis;    fault line selection;   

DOI  :  10.1109/ACCESS.2020.3027002

来源: DOAJ

【 摘 要 】

Series arc fault is a common phenomenon in the power system, it will directly affect the working reliability, but there is no mature method to detect it due to its concealment and chaos. Common detection methods that build on the arc fault eigenvectors obtained by manual analysis are subjective and incomprehensive. A series arc fault diagnosis and line selection method based on recurrent neural network (RNN) for a multi-load system was proposed in this paper. Firstly, a series arc fault experiment under a multi-load system was carried out, the training set and test set were built by using the data obtained from the experiment. Then, the RNN model was built, trained, and tested through the training set and test set. Finally, the fast-continuous detection method and the probability-based classification result correction method were proposed, and the detection speed and accuracy were improved much further. The results show that the proposed method is effective for diagnosing series arc fault and line selection under a multi-load system, without analysis of arc fault characteristics.

【 授权许可】

Unknown