Fault recognition method of smart grid data acquisition system based on FNN and sequential DS fusion. Issue 1 (14th February 2021)

• Record Type: Journal Article
• Authors: Qiao, Hanzhe; Ge, Quanbo; Jiang, Haoyu; Li, Ziyi; You, Zilong; Zhang, Jianmin; Bi, Fengjuan; Yu, Chunlei
• Is Part Of: Cognitive computation and systems. Volume 3:Issue 1(2021)
• Page Start: 28
• Page End: 36
• Publication Date: 2021-02-14
• Subjects: statistical analysis -- fuzzy neural nets -- feature extraction -- smart power grids -- condition monitoring -- fault diagnosis -- learning (artificial intelligence) -- data acquisition -- data fusion -- power engineering computing; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12006
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: It is of significant practical importance to ensure the operational safety of the smart grid, which requires real‐time fault diagnosis and identifying what causes it based on an enormous amount of data. This article further studies the intelligent fault‐identification method based on the combination of multi‐machine learning methods on the bases of researching on Fault Diagnosis of Smart Grid Data Acquisition System. Firstly, we should apply statistical analysis and feature extraction for fault data. Then, we can use fuzzy neural network (FNN) to calculate the probability of fault prediction of power distribution stations, manufacturers and operation businesses, and use the membership function to calculate the corresponding fault membership and uncertainty. Secondly, it makes use of Dempster/Shafer (DS) evidence sequential fusion method to realize fault membership fusion, and gives the corresponding decision criteria for failure causes. Thirdly, a fault‐identification method of smart grid data‐acquisition system is established based on FNN and DS Evidence Fusion. Finally, the experimental results based on the actual operation data of smart grid show that the new method has a very good application effect at fault cause identification.