A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy
This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PS...
Gespeichert in:
Veröffentlicht in: | International journal of electrical and computer engineering (Malacca, Malacca) Malacca), 2022-08, Vol.12 (4), p.4253 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 4 |
container_start_page | 4253 |
container_title | International journal of electrical and computer engineering (Malacca, Malacca) |
container_volume | 12 |
creator | Trong Le, Nghia Trieu Phung, Tan Huy Quyen, Anh Phung Nguyen, Bao Long Ngoc Nguyen, Au |
description | This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique. |
doi_str_mv | 10.11591/ijece.v12i4.pp4253-4263 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2766671849</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2766671849</sourcerecordid><originalsourceid>FETCH-LOGICAL-c202t-1855379a2bc5ed84c52dbc4d8682a5c2688cb523db7f6523d0c2e339e2f4eeb43</originalsourceid><addsrcrecordid>eNotkMlOwzAURSMEEhX0HyyxTolfPGVZVUxSJTawthzbaVzSONguVfl60oTVfdK9bzpZhnCxwphW-NHtrbarHwyOrIaBAC1zAqy8yhbAAXKgXFyPdSFELnghbrNljK4uCOGk4IwustMatec6OIPUMASvdIt8g1RIrnHaqQ719hgmSScfvvLhYunOonhS4YD8kNzB_arkfI9Ut_PBpfaAGh9ma-zsvDIottYY1-9QTEEluzvfZzeN6qJd_utd9vn89LF5zbfvL2-b9TbXUEDKsaC05JWCWlNrBNEUTK2JEUyAohqYELqmUJqaN-yihQZblpWFhlhbk_Iue5jnjs99H21Mcu-PoR9XSuCMMY4FqcaUmFM6-BiDbeQQxuPDWeJCTqTlRFpOpOVMWl5Il38E1ne3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2766671849</pqid></control><display><type>article</type><title>A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Trong Le, Nghia ; Trieu Phung, Tan ; Huy Quyen, Anh ; Phung Nguyen, Bao Long ; Ngoc Nguyen, Au</creator><creatorcontrib>Trong Le, Nghia ; Trieu Phung, Tan ; Huy Quyen, Anh ; Phung Nguyen, Bao Long ; Ngoc Nguyen, Au</creatorcontrib><description>This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.</description><identifier>ISSN: 2088-8708</identifier><identifier>EISSN: 2722-2578</identifier><identifier>EISSN: 2088-8708</identifier><identifier>DOI: 10.11591/ijece.v12i4.pp4253-4263</identifier><language>eng</language><publisher>Yogyakarta: IAES Institute of Advanced Engineering and Science</publisher><subject>Algorithms ; Artificial neural networks ; Electric power distribution ; Generators ; Load shedding ; Neural networks ; Optimization techniques ; Outages ; Particle swarm optimization ; Training</subject><ispartof>International journal of electrical and computer engineering (Malacca, Malacca), 2022-08, Vol.12 (4), p.4253</ispartof><rights>Copyright IAES Institute of Advanced Engineering and Science 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c202t-1855379a2bc5ed84c52dbc4d8682a5c2688cb523db7f6523d0c2e339e2f4eeb43</citedby><orcidid>0000-0002-2245-8755 ; 0000-0001-5007-9157 ; 0000-0002-4337-7014 ; 0000-0001-9617-6008 ; 0000-0001-8946-1302</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Trong Le, Nghia</creatorcontrib><creatorcontrib>Trieu Phung, Tan</creatorcontrib><creatorcontrib>Huy Quyen, Anh</creatorcontrib><creatorcontrib>Phung Nguyen, Bao Long</creatorcontrib><creatorcontrib>Ngoc Nguyen, Au</creatorcontrib><title>A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy</title><title>International journal of electrical and computer engineering (Malacca, Malacca)</title><description>This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Electric power distribution</subject><subject>Generators</subject><subject>Load shedding</subject><subject>Neural networks</subject><subject>Optimization techniques</subject><subject>Outages</subject><subject>Particle swarm optimization</subject><subject>Training</subject><issn>2088-8708</issn><issn>2722-2578</issn><issn>2088-8708</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNotkMlOwzAURSMEEhX0HyyxTolfPGVZVUxSJTawthzbaVzSONguVfl60oTVfdK9bzpZhnCxwphW-NHtrbarHwyOrIaBAC1zAqy8yhbAAXKgXFyPdSFELnghbrNljK4uCOGk4IwustMatec6OIPUMASvdIt8g1RIrnHaqQ719hgmSScfvvLhYunOonhS4YD8kNzB_arkfI9Ut_PBpfaAGh9ma-zsvDIottYY1-9QTEEluzvfZzeN6qJd_utd9vn89LF5zbfvL2-b9TbXUEDKsaC05JWCWlNrBNEUTK2JEUyAohqYELqmUJqaN-yihQZblpWFhlhbk_Iue5jnjs99H21Mcu-PoR9XSuCMMY4FqcaUmFM6-BiDbeQQxuPDWeJCTqTlRFpOpOVMWl5Il38E1ne3</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Trong Le, Nghia</creator><creator>Trieu Phung, Tan</creator><creator>Huy Quyen, Anh</creator><creator>Phung Nguyen, Bao Long</creator><creator>Ngoc Nguyen, Au</creator><general>IAES Institute of Advanced Engineering and Science</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-2245-8755</orcidid><orcidid>https://orcid.org/0000-0001-5007-9157</orcidid><orcidid>https://orcid.org/0000-0002-4337-7014</orcidid><orcidid>https://orcid.org/0000-0001-9617-6008</orcidid><orcidid>https://orcid.org/0000-0001-8946-1302</orcidid></search><sort><creationdate>20220801</creationdate><title>A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy</title><author>Trong Le, Nghia ; Trieu Phung, Tan ; Huy Quyen, Anh ; Phung Nguyen, Bao Long ; Ngoc Nguyen, Au</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c202t-1855379a2bc5ed84c52dbc4d8682a5c2688cb523db7f6523d0c2e339e2f4eeb43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Electric power distribution</topic><topic>Generators</topic><topic>Load shedding</topic><topic>Neural networks</topic><topic>Optimization techniques</topic><topic>Outages</topic><topic>Particle swarm optimization</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Trong Le, Nghia</creatorcontrib><creatorcontrib>Trieu Phung, Tan</creatorcontrib><creatorcontrib>Huy Quyen, Anh</creatorcontrib><creatorcontrib>Phung Nguyen, Bao Long</creatorcontrib><creatorcontrib>Ngoc Nguyen, Au</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East & South Asia Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of electrical and computer engineering (Malacca, Malacca)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trong Le, Nghia</au><au>Trieu Phung, Tan</au><au>Huy Quyen, Anh</au><au>Phung Nguyen, Bao Long</au><au>Ngoc Nguyen, Au</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy</atitle><jtitle>International journal of electrical and computer engineering (Malacca, Malacca)</jtitle><date>2022-08-01</date><risdate>2022</risdate><volume>12</volume><issue>4</issue><spage>4253</spage><pages>4253-</pages><issn>2088-8708</issn><eissn>2722-2578</eissn><eissn>2088-8708</eissn><abstract>This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.</abstract><cop>Yogyakarta</cop><pub>IAES Institute of Advanced Engineering and Science</pub><doi>10.11591/ijece.v12i4.pp4253-4263</doi><orcidid>https://orcid.org/0000-0002-2245-8755</orcidid><orcidid>https://orcid.org/0000-0001-5007-9157</orcidid><orcidid>https://orcid.org/0000-0002-4337-7014</orcidid><orcidid>https://orcid.org/0000-0001-9617-6008</orcidid><orcidid>https://orcid.org/0000-0001-8946-1302</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2088-8708 |
ispartof | International journal of electrical and computer engineering (Malacca, Malacca), 2022-08, Vol.12 (4), p.4253 |
issn | 2088-8708 2722-2578 2088-8708 |
language | eng |
recordid | cdi_proquest_journals_2766671849 |
source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Algorithms Artificial neural networks Electric power distribution Generators Load shedding Neural networks Optimization techniques Outages Particle swarm optimization Training |
title | A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T02%3A11%3A29IST&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=A%20hybrid%20approach%20of%20artificial%20neural%20network-particle%20swarm%20optimization%20algorithm%20for%20optimal%20load%20shedding%20strategy&rft.jtitle=International%20journal%20of%20electrical%20and%20computer%20engineering%20(Malacca,%20Malacca)&rft.au=Trong%20Le,%20Nghia&rft.date=2022-08-01&rft.volume=12&rft.issue=4&rft.spage=4253&rft.pages=4253-&rft.issn=2088-8708&rft.eissn=2722-2578&rft_id=info:doi/10.11591/ijece.v12i4.pp4253-4263&rft_dat=%3Cproquest_cross%3E2766671849%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=2766671849&rft_id=info:pmid/&rfr_iscdi=true |