A cable fault recognition method based on a deep belief network. (October 2018)

• Record Type: Journal Article
• Authors: qin, Xuebin; Zhang, Yizhe; Mei, Wang; Dong, Gang; Gao, Jun; Wang, Pai; Deng, Jun; Pan, Hongguang
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 452
• Page End: 464
• Publication Date: 2018-10
• Subjects: Cable fault -- Online recognition -- Deep learning -- Deep belief network -- Simulation model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.043
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: To meet the requirement of online diagnosis of a cable fault, certain problems should be addressed. Therefore, in this paper, we propose an online cable fault diagnosis method. First, we establish a simulation model of an underground cable distribution system for collecting fault signals. Second, a deep belief network (DBN) is created by the deep learning theory for identifying a cable fault. Finally, we extract the characteristics of the fault signal and classify them into a large number of fault data automatically. A comparison of the results of the cable fault recognition with the proposed method and conventional shallow neural network shows that the DBN is of 97.8%, the conventional back propagation (BP) network is of 86.6%, ACCLN is of 94.1%, which demonstrate that the DBN-based cable fault recognition method has distinct advantages compared with a shallow neural network.