SR-GNN Based Fault Classification and Location in Power Distribution Network

Haojie Mo, Yonggang Peng (), Wei Wei, Wei Xi and Tiantian Cai
Additional contact information
Haojie Mo: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yonggang Peng: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Wei Wei: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Wei Xi: Electric Power Research Institute, China Southern Power Grid, Guangzhou 510700, China
Tiantian Cai: Electric Power Research Institute, China Southern Power Grid, Guangzhou 510700, China

Energies, 2022, vol. 16, issue 1, 1-15

Abstract: Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

Keywords: fault classification; fault location; distribution systems; super-resolution; graph neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/433/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/433/ (text/html)

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:gam:jeners:v:16:y:2022:i:1:p:433-:d:1020310

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().