DocumentCode :
3099291
Title :
Fault Detection and Identification Based on DFS in Electric Power Network
Author :
Yagang, Zhang ; Jinfang, Zhang ; Jing, Ma ; Zengping, Wang
Author_Institution :
Key Lab. of Power Syst. Protection & Dynamic Security Monitoring & Control under Minist. of Educ., North China Electr. Power Univ., Baoding
fYear :
2008
fDate :
21-22 Dec. 2008
Firstpage :
742
Lastpage :
745
Abstract :
In this paper, we adopt a novel topological approach to fault detection and identification. In our researches, global information will be introduced into the backup protection system, we are using mainly DFS of graph theory algorithms to resolve fast and exact discrimination of faulty components and faulty sections, and finally accomplish fault detection and identification in electric power networks. Graph theory algorithms can be used to model many different physical and abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. In the study of fault detection and identification, graph theory algorithms must also have a good prospect of application.
Keywords :
fault location; graph theory; power distribution faults; power distribution protection; power transmission faults; power transmission protection; tree searching; DFS algorithm; backup protection system; depth-first search; electric power network; fault detection; fault identification; graph theory; Electrical fault detection; Fault detection; Fault diagnosis; Graph theory; Mathematical model; Monitoring; Phasor measurement units; Power grids; Power system modeling; Power system protection; DFS; PMU; fault detection and identification; graph theory; phasor measurement unit;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3530-2
Electronic_ISBN :
978-1-4244-3531-9
Type :
conf
DOI :
10.1109/KAMW.2008.4810597
Filename :
4810597
Link To Document :
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