Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems
One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural n...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on power delivery 2006-07, Vol.21 (3), p.1735-1742 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1742 |
|---|---|
| container_issue | 3 |
| container_start_page | 1735 |
| container_title | IEEE transactions on power delivery |
| container_volume | 21 |
| creator | Salazar, H. Gallego, R. Romero, R. |
| description | One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment. |
| doi_str_mv | 10.1109/TPWRD.2006.875854 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671399532</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1645224</ieee_id><sourcerecordid>1671399532</sourcerecordid><originalsourceid>FETCH-LOGICAL-c355t-af1667dde06620124ad805861faf81572303d1596d216133ab220a96a59914503</originalsourceid><addsrcrecordid>eNpdkF1LHDEYhYNUcLv2B0hvQqHgzaxvPie5XLYfCoIiFi9DzCQanc1skwxl_72zu4LQq8PL-5zD4SB0RmBBCOiL-9uHux8LCiAXqhVK8CM0I5q1DaegPqEZKCUapdv2BH0u5QUAOGiYoddlrjFEF22Pkx_zXuq_Ib8WbFOHXT-W6nNMT7h695zi39FPn82mj77DMeH67HH2bkghPk32GoeEh4C7WGqOj-P-LtspY11O0XGwffFf3nWO_vz6eb-6bK5vfl-tlteNY0LUxgYiZdt1HqSkQCi3nQKhJAk2KCJayoB1RGjZUSIJY_aRUrBaWqE14QLYHJ0fcjd52NWtZh2L831vkx_GYohsCdNaMDqh3_5DX4Yxp6mdUVIyxYHzCSIHyOWhlOyD2eS4tnlrCJjd-ma_vtmtbw7rT57v78G2ONuHbJOL5cPYaiYZ23X9euCi9_7jLbmglLM3RlSOSw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>866384044</pqid></control><display><type>article</type><title>Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems</title><source>IEEE Electronic Library (IEL)</source><creator>Salazar, H. ; Gallego, R. ; Romero, R.</creator><creatorcontrib>Salazar, H. ; Gallego, R. ; Romero, R.</creatorcontrib><description>One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.</description><identifier>ISSN: 0885-8977</identifier><identifier>EISSN: 1937-4208</identifier><identifier>DOI: 10.1109/TPWRD.2006.875854</identifier><identifier>CODEN: ITPDE5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Artificial neural networks ; Artificial neural networks (ANNs) ; Clustering ; clustering techniques ; Electrical engineering. Electrical power engineering ; Electrical power engineering ; Exact sciences and technology ; feeder reconfiguration ; Feeders ; Intelligent networks ; Load flow ; Load flow analysis ; Mathematical model ; Mathematical models ; Methodology ; Miscellaneous ; Mixed integer ; Network topology ; Neural networks ; optimization techniques ; Power networks and lines ; Power system restoration ; Reconfiguration ; Student members</subject><ispartof>IEEE transactions on power delivery, 2006-07, Vol.21 (3), p.1735-1742</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-af1667dde06620124ad805861faf81572303d1596d216133ab220a96a59914503</citedby><cites>FETCH-LOGICAL-c355t-af1667dde06620124ad805861faf81572303d1596d216133ab220a96a59914503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1645224$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1645224$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17936330$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Salazar, H.</creatorcontrib><creatorcontrib>Gallego, R.</creatorcontrib><creatorcontrib>Romero, R.</creatorcontrib><title>Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems</title><title>IEEE transactions on power delivery</title><addtitle>TPWRD</addtitle><description>One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Artificial neural networks (ANNs)</subject><subject>Clustering</subject><subject>clustering techniques</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical power engineering</subject><subject>Exact sciences and technology</subject><subject>feeder reconfiguration</subject><subject>Feeders</subject><subject>Intelligent networks</subject><subject>Load flow</subject><subject>Load flow analysis</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Methodology</subject><subject>Miscellaneous</subject><subject>Mixed integer</subject><subject>Network topology</subject><subject>Neural networks</subject><subject>optimization techniques</subject><subject>Power networks and lines</subject><subject>Power system restoration</subject><subject>Reconfiguration</subject><subject>Student members</subject><issn>0885-8977</issn><issn>1937-4208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkF1LHDEYhYNUcLv2B0hvQqHgzaxvPie5XLYfCoIiFi9DzCQanc1skwxl_72zu4LQq8PL-5zD4SB0RmBBCOiL-9uHux8LCiAXqhVK8CM0I5q1DaegPqEZKCUapdv2BH0u5QUAOGiYoddlrjFEF22Pkx_zXuq_Ib8WbFOHXT-W6nNMT7h695zi39FPn82mj77DMeH67HH2bkghPk32GoeEh4C7WGqOj-P-LtspY11O0XGwffFf3nWO_vz6eb-6bK5vfl-tlteNY0LUxgYiZdt1HqSkQCi3nQKhJAk2KCJayoB1RGjZUSIJY_aRUrBaWqE14QLYHJ0fcjd52NWtZh2L831vkx_GYohsCdNaMDqh3_5DX4Yxp6mdUVIyxYHzCSIHyOWhlOyD2eS4tnlrCJjd-ma_vtmtbw7rT57v78G2ONuHbJOL5cPYaiYZ23X9euCi9_7jLbmglLM3RlSOSw</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Salazar, H.</creator><creator>Gallego, R.</creator><creator>Romero, R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>20060701</creationdate><title>Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems</title><author>Salazar, H. ; Gallego, R. ; Romero, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-af1667dde06620124ad805861faf81572303d1596d216133ab220a96a59914503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Artificial neural networks (ANNs)</topic><topic>Clustering</topic><topic>clustering techniques</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical power engineering</topic><topic>Exact sciences and technology</topic><topic>feeder reconfiguration</topic><topic>Feeders</topic><topic>Intelligent networks</topic><topic>Load flow</topic><topic>Load flow analysis</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Methodology</topic><topic>Miscellaneous</topic><topic>Mixed integer</topic><topic>Network topology</topic><topic>Neural networks</topic><topic>optimization techniques</topic><topic>Power networks and lines</topic><topic>Power system restoration</topic><topic>Reconfiguration</topic><topic>Student members</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Salazar, H.</creatorcontrib><creatorcontrib>Gallego, R.</creatorcontrib><creatorcontrib>Romero, R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power delivery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Salazar, H.</au><au>Gallego, R.</au><au>Romero, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems</atitle><jtitle>IEEE transactions on power delivery</jtitle><stitle>TPWRD</stitle><date>2006-07-01</date><risdate>2006</risdate><volume>21</volume><issue>3</issue><spage>1735</spage><epage>1742</epage><pages>1735-1742</pages><issn>0885-8977</issn><eissn>1937-4208</eissn><coden>ITPDE5</coden><abstract>One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TPWRD.2006.875854</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0885-8977 |
| ispartof | IEEE transactions on power delivery, 2006-07, Vol.21 (3), p.1735-1742 |
| issn | 0885-8977 1937-4208 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1671399532 |
| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Artificial neural networks Artificial neural networks (ANNs) Clustering clustering techniques Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology feeder reconfiguration Feeders Intelligent networks Load flow Load flow analysis Mathematical model Mathematical models Methodology Miscellaneous Mixed integer Network topology Neural networks optimization techniques Power networks and lines Power system restoration Reconfiguration Student members |
| title | Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T06%3A41%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20neural%20networks%20and%20clustering%20techniques%20applied%20in%20the%20reconfiguration%20of%20distribution%20systems&rft.jtitle=IEEE%20transactions%20on%20power%20delivery&rft.au=Salazar,%20H.&rft.date=2006-07-01&rft.volume=21&rft.issue=3&rft.spage=1735&rft.epage=1742&rft.pages=1735-1742&rft.issn=0885-8977&rft.eissn=1937-4208&rft.coden=ITPDE5&rft_id=info:doi/10.1109/TPWRD.2006.875854&rft_dat=%3Cproquest_RIE%3E1671399532%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=866384044&rft_id=info:pmid/&rft_ieee_id=1645224&rfr_iscdi=true |