Fault recovery system for smart grid based on machine statistical learning

Min Zhu, Juncheng Peng and Lixing Zhou

International Journal of Critical Infrastructures, 2021, vol. 17, issue 3, 271-287

Abstract: In order to overcome the problems of poor robustness, low-accuracy and long time-consuming when traditional system recovers power grid faults, a fault recovery system based on machine statistical learning is designed. The system framework consists of sensing layer, network layer and application layer. Through the overall framework of the system, the hardware of the system is designed, including data acquisition device block, transmission device, analysis module and display device. In the software part, fault acquisition subroutine, fault location subroutine and fault type identification subroutine are designed to obtain accurate fault data. Finally, machine statistical learning method is used to complete the design of fault recovery subroutine of smart grid, recover the obtained fault data and realise the design of fault recovery system of smart grid. The results show that the robustness, accuracy and time-consumption of the system are improved, and the problems existing in the traditional system are solved.

Keywords: machine statistical learning; smart grid; fault; recovery system. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=118196 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcist:v:17:y:2021:i:3:p:271-287

Access Statistics for this article

More articles in International Journal of Critical Infrastructures from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().