Power Grid Partitioning Based on Functional Community Structure
Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure...
Gespeichert in:
Veröffentlicht in: | IEEE access 2019, Vol.7, p.152624-152634 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 152634 |
---|---|
container_issue | |
container_start_page | 152624 |
container_title | IEEE access |
container_volume | 7 |
creator | Zhao, Chuanzhi Zhao, Jintang Wu, Chunchao Wang, Xiaoliang Xue, Fei Lu, Shaofeng |
description | Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure based on an extended weighted network model. An extended adjacency matrix is used to represent an extended weighted complex network model based on coupling strength rather than the conventional adjacency matrix. Meanwhile, we upgraded the Newman fast algorithm of community detection for establishing a novel power grid partitioning algorithm. The electrical coupling strength (ECS) is defined to better reflect electrical characteristics between any two nodes in power grid. Modularity is also redefined as electrical modularity based on ECS. The Newman fast algorithm is upgraded with electrical modularity maximization as the objective to detect functional communities in power grids. A case study on IEEE test systems with 30, 39, 118, 300 buses and one Italian power network demonstrates the rationality of the extended weighted network model and partitioning algorithm. |
doi_str_mv | 10.1109/ACCESS.2019.2948606 |
format | Article |
fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_8878124</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8878124</ieee_id><doaj_id>oai_doaj_org_article_a5a9fd0aafc347649ce84f1a44bb6d59</doaj_id><sourcerecordid>2455617503</sourcerecordid><originalsourceid>FETCH-LOGICAL-c458t-3ef532cf0093556f68dfd07aeed0b75917ffc91cb5d10ee6b03262a2d15c79703</originalsourceid><addsrcrecordid>eNpNUE1rwkAQDaWFivUXeAn0HLvfmz0VG9QKQgXb87LZD4lo1m4Siv--m0akc5nhMe_Nm5ckUwhmEALxMi-KxW43QwCKGRIkZ4DdJSMEmcgwxez-3_yYTJrmAGLlEaJ8lLxu_Y8N6SpUJt2q0FZt5euq3qdvqrEm9XW67Grdg-qYFv506uqqvaS7NnS67YJ9Sh6cOjZ2cu3j5Gu5-Czes83Hal3MN5kmNG8zbB3FSDsABKaUOZYbZwBX1hpQciogd04LqEtqILCWlQAjhhQykGouOMDjZD3oGq8O8hyqkwoX6VUl_wAf9rJ3r49WKqpEFFfKaUw4I0LbnDioCClLZqiIWs-D1jn47842rTz4LsQHG4lIdAc5BThu4WFLB980wbrbVQhkH7wcgpd98PIafGRNB1Zlrb0x8pznEBH8Cw-ffpc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455617503</pqid></control><display><type>article</type><title>Power Grid Partitioning Based on Functional Community Structure</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zhao, Chuanzhi ; Zhao, Jintang ; Wu, Chunchao ; Wang, Xiaoliang ; Xue, Fei ; Lu, Shaofeng</creator><creatorcontrib>Zhao, Chuanzhi ; Zhao, Jintang ; Wu, Chunchao ; Wang, Xiaoliang ; Xue, Fei ; Lu, Shaofeng</creatorcontrib><description>Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure based on an extended weighted network model. An extended adjacency matrix is used to represent an extended weighted complex network model based on coupling strength rather than the conventional adjacency matrix. Meanwhile, we upgraded the Newman fast algorithm of community detection for establishing a novel power grid partitioning algorithm. The electrical coupling strength (ECS) is defined to better reflect electrical characteristics between any two nodes in power grid. Modularity is also redefined as electrical modularity based on ECS. The Newman fast algorithm is upgraded with electrical modularity maximization as the objective to detect functional communities in power grids. A case study on IEEE test systems with 30, 39, 118, 300 buses and one Italian power network demonstrates the rationality of the extended weighted network model and partitioning algorithm.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2948606</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Clustering algorithms ; community detection ; Complex network ; Complex networks ; Coupling ; Couplings ; Electric power grids ; electrical coupling strength ; functional community ; Impedance ; Modularity ; Newman fast algorithm ; Partitioning ; Partitioning algorithms ; power grid partition ; Power grids ; Transmission line matrix methods</subject><ispartof>IEEE access, 2019, Vol.7, p.152624-152634</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-3ef532cf0093556f68dfd07aeed0b75917ffc91cb5d10ee6b03262a2d15c79703</citedby><cites>FETCH-LOGICAL-c458t-3ef532cf0093556f68dfd07aeed0b75917ffc91cb5d10ee6b03262a2d15c79703</cites><orcidid>0000-0003-3567-258X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8878124$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Zhao, Chuanzhi</creatorcontrib><creatorcontrib>Zhao, Jintang</creatorcontrib><creatorcontrib>Wu, Chunchao</creatorcontrib><creatorcontrib>Wang, Xiaoliang</creatorcontrib><creatorcontrib>Xue, Fei</creatorcontrib><creatorcontrib>Lu, Shaofeng</creatorcontrib><title>Power Grid Partitioning Based on Functional Community Structure</title><title>IEEE access</title><addtitle>Access</addtitle><description>Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure based on an extended weighted network model. An extended adjacency matrix is used to represent an extended weighted complex network model based on coupling strength rather than the conventional adjacency matrix. Meanwhile, we upgraded the Newman fast algorithm of community detection for establishing a novel power grid partitioning algorithm. The electrical coupling strength (ECS) is defined to better reflect electrical characteristics between any two nodes in power grid. Modularity is also redefined as electrical modularity based on ECS. The Newman fast algorithm is upgraded with electrical modularity maximization as the objective to detect functional communities in power grids. A case study on IEEE test systems with 30, 39, 118, 300 buses and one Italian power network demonstrates the rationality of the extended weighted network model and partitioning algorithm.</description><subject>Algorithms</subject><subject>Clustering algorithms</subject><subject>community detection</subject><subject>Complex network</subject><subject>Complex networks</subject><subject>Coupling</subject><subject>Couplings</subject><subject>Electric power grids</subject><subject>electrical coupling strength</subject><subject>functional community</subject><subject>Impedance</subject><subject>Modularity</subject><subject>Newman fast algorithm</subject><subject>Partitioning</subject><subject>Partitioning algorithms</subject><subject>power grid partition</subject><subject>Power grids</subject><subject>Transmission line matrix methods</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1rwkAQDaWFivUXeAn0HLvfmz0VG9QKQgXb87LZD4lo1m4Siv--m0akc5nhMe_Nm5ckUwhmEALxMi-KxW43QwCKGRIkZ4DdJSMEmcgwxez-3_yYTJrmAGLlEaJ8lLxu_Y8N6SpUJt2q0FZt5euq3qdvqrEm9XW67Grdg-qYFv506uqqvaS7NnS67YJ9Sh6cOjZ2cu3j5Gu5-Czes83Hal3MN5kmNG8zbB3FSDsABKaUOZYbZwBX1hpQciogd04LqEtqILCWlQAjhhQykGouOMDjZD3oGq8O8hyqkwoX6VUl_wAf9rJ3r49WKqpEFFfKaUw4I0LbnDioCClLZqiIWs-D1jn47842rTz4LsQHG4lIdAc5BThu4WFLB980wbrbVQhkH7wcgpd98PIafGRNB1Zlrb0x8pznEBH8Cw-ffpc</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Zhao, Chuanzhi</creator><creator>Zhao, Jintang</creator><creator>Wu, Chunchao</creator><creator>Wang, Xiaoliang</creator><creator>Xue, Fei</creator><creator>Lu, Shaofeng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3567-258X</orcidid></search><sort><creationdate>2019</creationdate><title>Power Grid Partitioning Based on Functional Community Structure</title><author>Zhao, Chuanzhi ; Zhao, Jintang ; Wu, Chunchao ; Wang, Xiaoliang ; Xue, Fei ; Lu, Shaofeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-3ef532cf0093556f68dfd07aeed0b75917ffc91cb5d10ee6b03262a2d15c79703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Clustering algorithms</topic><topic>community detection</topic><topic>Complex network</topic><topic>Complex networks</topic><topic>Coupling</topic><topic>Couplings</topic><topic>Electric power grids</topic><topic>electrical coupling strength</topic><topic>functional community</topic><topic>Impedance</topic><topic>Modularity</topic><topic>Newman fast algorithm</topic><topic>Partitioning</topic><topic>Partitioning algorithms</topic><topic>power grid partition</topic><topic>Power grids</topic><topic>Transmission line matrix methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Chuanzhi</creatorcontrib><creatorcontrib>Zhao, Jintang</creatorcontrib><creatorcontrib>Wu, Chunchao</creatorcontrib><creatorcontrib>Wang, Xiaoliang</creatorcontrib><creatorcontrib>Xue, Fei</creatorcontrib><creatorcontrib>Lu, Shaofeng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Chuanzhi</au><au>Zhao, Jintang</au><au>Wu, Chunchao</au><au>Wang, Xiaoliang</au><au>Xue, Fei</au><au>Lu, Shaofeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Power Grid Partitioning Based on Functional Community Structure</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2019</date><risdate>2019</risdate><volume>7</volume><spage>152624</spage><epage>152634</epage><pages>152624-152634</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Network partitioning is a popular research topic. Not all available partitioning methods are equally suitable for power grids. Community detection is a critical issue in complex network theory, and power grid is a typical type of complex network. This paper proposes a functional community structure based on an extended weighted network model. An extended adjacency matrix is used to represent an extended weighted complex network model based on coupling strength rather than the conventional adjacency matrix. Meanwhile, we upgraded the Newman fast algorithm of community detection for establishing a novel power grid partitioning algorithm. The electrical coupling strength (ECS) is defined to better reflect electrical characteristics between any two nodes in power grid. Modularity is also redefined as electrical modularity based on ECS. The Newman fast algorithm is upgraded with electrical modularity maximization as the objective to detect functional communities in power grids. A case study on IEEE test systems with 30, 39, 118, 300 buses and one Italian power network demonstrates the rationality of the extended weighted network model and partitioning algorithm.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2948606</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-3567-258X</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2019, Vol.7, p.152624-152634 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_ieee_primary_8878124 |
source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Algorithms Clustering algorithms community detection Complex network Complex networks Coupling Couplings Electric power grids electrical coupling strength functional community Impedance Modularity Newman fast algorithm Partitioning Partitioning algorithms power grid partition Power grids Transmission line matrix methods |
title | Power Grid Partitioning Based on Functional Community Structure |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T16%3A25%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Power%20Grid%20Partitioning%20Based%20on%20Functional%20Community%20Structure&rft.jtitle=IEEE%20access&rft.au=Zhao,%20Chuanzhi&rft.date=2019&rft.volume=7&rft.spage=152624&rft.epage=152634&rft.pages=152624-152634&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2019.2948606&rft_dat=%3Cproquest_ieee_%3E2455617503%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2455617503&rft_id=info:pmid/&rft_ieee_id=8878124&rft_doaj_id=oai_doaj_org_article_a5a9fd0aafc347649ce84f1a44bb6d59&rfr_iscdi=true |