Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms
•A GA-based method to improve reliability of distribution systems by reconfiguration.•Two objective functions proposed to address power quality and reliability issues.•The effectiveness is investigated on two standard test distribution systems.•Application results are promising when compared with ot...
Gespeichert in:
| Veröffentlicht in: | International journal of electrical power & energy systems 2014-01, Vol.54, p.664-671 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 671 |
|---|---|
| container_issue | |
| container_start_page | 664 |
| container_title | International journal of electrical power & energy systems |
| container_volume | 54 |
| creator | Gupta, Nikhil Swarnkar, Anil Niazi, K.R. |
| description | •A GA-based method to improve reliability of distribution systems by reconfiguration.•Two objective functions proposed to address power quality and reliability issues.•The effectiveness is investigated on two standard test distribution systems.•Application results are promising when compared with other existing method.
This paper presents an efficient Genetic Algorithms (GAs) based method to improve the reliability and power quality of distribution systems using network reconfiguration. Two new objective functions are formulated to address power quality and reliability issues for the reconfiguration problem. Various power quality and reliability objectives such as feeder power loss, system’s node voltage deviation, system’s average interruption frequency index, system’s average interruption unavailability index and energy not supplied are transformed into a single objective function. This single objective problem is then solved using the GA-based method. The effectiveness of the proposed objective functions has been investigated on two different standard test distribution systems. Application results are promising when compared with other existing method. |
| doi_str_mv | 10.1016/j.ijepes.2013.08.016 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1513462744</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0142061513003578</els_id><sourcerecordid>1513462744</sourcerecordid><originalsourceid>FETCH-LOGICAL-c369t-96e067951a643cc4685e0b7ee3111b82f154d534194e16b30a78d59a58894fdb3</originalsourceid><addsrcrecordid>eNp9kE9v1DAQxS0EEkvhG3DIBYlLUk_i2PEFqSpQkCpxgbPlOJNltom9tZ1W_fa43YpjT6N5-r358xj7CLwBDvL80NABj5ialkPX8KEp4iu2g0HpuutBvWY7DqKtuYT-LXuX0oFzrrRod-zmK6UcadwyBV95zPch3lQRXfAz7bdon_Q5xOoY7jFWt5tdKD9U1k-FWsiO9NTTeozhDlf0udoS-X11hWUauepi2YdI-e-a3rM3s10SfniuZ-zP92-_L3_U17-ufl5eXNeukzrXWiKXSvdgpeicE3LokY8KsQOAcWhn6MXUdwK0QJBjx60apl7bfhi0mKexO2OfT3PLSbcbpmxWSg6XxXoMWzLQQydkq4QoqDihLoaUIs7mGGm18cEAN4_ZmoM5ZWseszV8MEUstk_PG2xydpmj9Y7Sf2-rlNbAdeG-nDgs794RRpMcoXc4UYk4mynQy4v-AQTFk3E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1513462744</pqid></control><display><type>article</type><title>Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms</title><source>Access via ScienceDirect (Elsevier)</source><creator>Gupta, Nikhil ; Swarnkar, Anil ; Niazi, K.R.</creator><creatorcontrib>Gupta, Nikhil ; Swarnkar, Anil ; Niazi, K.R.</creatorcontrib><description>•A GA-based method to improve reliability of distribution systems by reconfiguration.•Two objective functions proposed to address power quality and reliability issues.•The effectiveness is investigated on two standard test distribution systems.•Application results are promising when compared with other existing method.
This paper presents an efficient Genetic Algorithms (GAs) based method to improve the reliability and power quality of distribution systems using network reconfiguration. Two new objective functions are formulated to address power quality and reliability issues for the reconfiguration problem. Various power quality and reliability objectives such as feeder power loss, system’s node voltage deviation, system’s average interruption frequency index, system’s average interruption unavailability index and energy not supplied are transformed into a single objective function. This single objective problem is then solved using the GA-based method. The effectiveness of the proposed objective functions has been investigated on two different standard test distribution systems. Application results are promising when compared with other existing method.</description><identifier>ISSN: 0142-0615</identifier><identifier>EISSN: 1879-3517</identifier><identifier>DOI: 10.1016/j.ijepes.2013.08.016</identifier><identifier>CODEN: IEPSDC</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Applied sciences ; Deviation ; Distribution network ; Electric potential ; Electric power generation ; Electrical engineering. Electrical power engineering ; Electrical power engineering ; Exact sciences and technology ; Genetic Algorithms ; Interruption ; Miscellaneous ; Networks ; Operation. Load control. Reliability ; Power electronics, power supplies ; Power networks and lines ; Power quality ; Reconfiguration ; Reliability ; Voltage</subject><ispartof>International journal of electrical power & energy systems, 2014-01, Vol.54, p.664-671</ispartof><rights>2013 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-96e067951a643cc4685e0b7ee3111b82f154d534194e16b30a78d59a58894fdb3</citedby><cites>FETCH-LOGICAL-c369t-96e067951a643cc4685e0b7ee3111b82f154d534194e16b30a78d59a58894fdb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijepes.2013.08.016$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,4024,27923,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27799109$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Gupta, Nikhil</creatorcontrib><creatorcontrib>Swarnkar, Anil</creatorcontrib><creatorcontrib>Niazi, K.R.</creatorcontrib><title>Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms</title><title>International journal of electrical power & energy systems</title><description>•A GA-based method to improve reliability of distribution systems by reconfiguration.•Two objective functions proposed to address power quality and reliability issues.•The effectiveness is investigated on two standard test distribution systems.•Application results are promising when compared with other existing method.
This paper presents an efficient Genetic Algorithms (GAs) based method to improve the reliability and power quality of distribution systems using network reconfiguration. Two new objective functions are formulated to address power quality and reliability issues for the reconfiguration problem. Various power quality and reliability objectives such as feeder power loss, system’s node voltage deviation, system’s average interruption frequency index, system’s average interruption unavailability index and energy not supplied are transformed into a single objective function. This single objective problem is then solved using the GA-based method. The effectiveness of the proposed objective functions has been investigated on two different standard test distribution systems. Application results are promising when compared with other existing method.</description><subject>Applied sciences</subject><subject>Deviation</subject><subject>Distribution network</subject><subject>Electric potential</subject><subject>Electric power generation</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical power engineering</subject><subject>Exact sciences and technology</subject><subject>Genetic Algorithms</subject><subject>Interruption</subject><subject>Miscellaneous</subject><subject>Networks</subject><subject>Operation. Load control. Reliability</subject><subject>Power electronics, power supplies</subject><subject>Power networks and lines</subject><subject>Power quality</subject><subject>Reconfiguration</subject><subject>Reliability</subject><subject>Voltage</subject><issn>0142-0615</issn><issn>1879-3517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kE9v1DAQxS0EEkvhG3DIBYlLUk_i2PEFqSpQkCpxgbPlOJNltom9tZ1W_fa43YpjT6N5-r358xj7CLwBDvL80NABj5ialkPX8KEp4iu2g0HpuutBvWY7DqKtuYT-LXuX0oFzrrRod-zmK6UcadwyBV95zPch3lQRXfAz7bdon_Q5xOoY7jFWt5tdKD9U1k-FWsiO9NTTeozhDlf0udoS-X11hWUauepi2YdI-e-a3rM3s10SfniuZ-zP92-_L3_U17-ufl5eXNeukzrXWiKXSvdgpeicE3LokY8KsQOAcWhn6MXUdwK0QJBjx60apl7bfhi0mKexO2OfT3PLSbcbpmxWSg6XxXoMWzLQQydkq4QoqDihLoaUIs7mGGm18cEAN4_ZmoM5ZWseszV8MEUstk_PG2xydpmj9Y7Sf2-rlNbAdeG-nDgs794RRpMcoXc4UYk4mynQy4v-AQTFk3E</recordid><startdate>201401</startdate><enddate>201401</enddate><creator>Gupta, Nikhil</creator><creator>Swarnkar, Anil</creator><creator>Niazi, K.R.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201401</creationdate><title>Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms</title><author>Gupta, Nikhil ; Swarnkar, Anil ; Niazi, K.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-96e067951a643cc4685e0b7ee3111b82f154d534194e16b30a78d59a58894fdb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Deviation</topic><topic>Distribution network</topic><topic>Electric potential</topic><topic>Electric power generation</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical power engineering</topic><topic>Exact sciences and technology</topic><topic>Genetic Algorithms</topic><topic>Interruption</topic><topic>Miscellaneous</topic><topic>Networks</topic><topic>Operation. Load control. Reliability</topic><topic>Power electronics, power supplies</topic><topic>Power networks and lines</topic><topic>Power quality</topic><topic>Reconfiguration</topic><topic>Reliability</topic><topic>Voltage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gupta, Nikhil</creatorcontrib><creatorcontrib>Swarnkar, Anil</creatorcontrib><creatorcontrib>Niazi, K.R.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of electrical power & energy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gupta, Nikhil</au><au>Swarnkar, Anil</au><au>Niazi, K.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms</atitle><jtitle>International journal of electrical power & energy systems</jtitle><date>2014-01</date><risdate>2014</risdate><volume>54</volume><spage>664</spage><epage>671</epage><pages>664-671</pages><issn>0142-0615</issn><eissn>1879-3517</eissn><coden>IEPSDC</coden><abstract>•A GA-based method to improve reliability of distribution systems by reconfiguration.•Two objective functions proposed to address power quality and reliability issues.•The effectiveness is investigated on two standard test distribution systems.•Application results are promising when compared with other existing method.
This paper presents an efficient Genetic Algorithms (GAs) based method to improve the reliability and power quality of distribution systems using network reconfiguration. Two new objective functions are formulated to address power quality and reliability issues for the reconfiguration problem. Various power quality and reliability objectives such as feeder power loss, system’s node voltage deviation, system’s average interruption frequency index, system’s average interruption unavailability index and energy not supplied are transformed into a single objective function. This single objective problem is then solved using the GA-based method. The effectiveness of the proposed objective functions has been investigated on two different standard test distribution systems. Application results are promising when compared with other existing method.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ijepes.2013.08.016</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0142-0615 |
| ispartof | International journal of electrical power & energy systems, 2014-01, Vol.54, p.664-671 |
| issn | 0142-0615 1879-3517 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1513462744 |
| source | Access via ScienceDirect (Elsevier) |
| subjects | Applied sciences Deviation Distribution network Electric potential Electric power generation Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Genetic Algorithms Interruption Miscellaneous Networks Operation. Load control. Reliability Power electronics, power supplies Power networks and lines Power quality Reconfiguration Reliability Voltage |
| title | Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T04%3A28%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Distribution%20network%20reconfiguration%20for%20power%20quality%20and%20reliability%20improvement%20using%20Genetic%20Algorithms&rft.jtitle=International%20journal%20of%20electrical%20power%20&%20energy%20systems&rft.au=Gupta,%20Nikhil&rft.date=2014-01&rft.volume=54&rft.spage=664&rft.epage=671&rft.pages=664-671&rft.issn=0142-0615&rft.eissn=1879-3517&rft.coden=IEPSDC&rft_id=info:doi/10.1016/j.ijepes.2013.08.016&rft_dat=%3Cproquest_cross%3E1513462744%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1513462744&rft_id=info:pmid/&rft_els_id=S0142061513003578&rfr_iscdi=true |