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Intentional islanding method based on community detection for distribution networks
- Source :
- IET Generation, Transmission and Distribution, 13(1)
- Publication Year :
- 2018
- Publisher :
- Institution of Engineering and Technology (IET), 2018.
-
Abstract
- Complex network theory is introduced to solve the islanding problem in an emergency of distribution networks. In this study, the authors put forward an intentional islanding method based on community detection. In this method, a new index has been defined called electrical edge betweenness, on the strength of edge betweenness in complex networks, which fuses electrical characteristics with topological features of actual power lines. Based on the index, the Girvan–Newman algorithm is employed to detect the community structure of distribution networks. Through referring to the modularity value (function Q) and coherent generator groups, they can get a reasonable amount and regions of communities. Then the whole distribution network can be partitioned into several self-sustainable islands meeting the stable operation constraints. The effectiveness of the authors’ proposed method is tested on a standard IEEE 118-bus system.
- Subjects :
- Computer science
020209 energy
Girvan–Newman algorithm
self-sustainable islands
electrical edge betweenness
electrical characteristics
islanding problem
Energy Engineering and Power Technology
intentional islanding method
02 engineering and technology
computer.software_genre
Betweenness centrality
community detection
0202 electrical engineering, electronic engineering, information engineering
Islanding
Electrical and Electronic Engineering
coherent generator groups
complex network theory
Modularity (networks)
stable operation constraints
standard IEEE 118-bus system
modularity value
edge betweenness strength
020208 electrical & electronic engineering
Function (mathematics)
Complex network
function Q
community structure detection
Electric power transmission
distribution networks
actual power lines
Girvan-Newman algorithm
topological features
Control and Systems Engineering
Enhanced Data Rates for GSM Evolution
Data mining
computer
Subjects
Details
- ISSN :
- 17518695 and 17518687
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- IET Generation, Transmission & Distribution
- Accession number :
- edsair.doi.dedup.....26d195077427511abeeda315ec86a8a7
- Full Text :
- https://doi.org/10.1049/iet-gtd.2018.5465