Optimal power flow using hybrid firefly and particle swarm optimization algorithm
In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Part...
Gespeichert in:
| Veröffentlicht in: | PloS one 2020-08, Vol.15 (8), p.e0235668-e0235668 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0235668 |
|---|---|
| container_issue | 8 |
| container_start_page | e0235668 |
| container_title | PloS one |
| container_volume | 15 |
| creator | Khan, Abdullah Hizam, Hashim Bin Abdul Wahab, Noor Izzri Lutfi Othman, Mohammad |
| description | In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems. |
| doi_str_mv | 10.1371/journal.pone.0235668 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2432420020</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A632112949</galeid><doaj_id>oai_doaj_org_article_54cfabf03052479394cb9fcadb13d4b3</doaj_id><sourcerecordid>A632112949</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-9d53e39fc843ef5cf2faf264589260552f71532f29b54cb3b784f034642324f03</originalsourceid><addsrcrecordid>eNqNkluL1DAYhoso7rr6D0QLgujFjDm3vRGWxcPAwuDxNqRt0mZIm5qkjuOvN3W6y1T2QnqRkO_53u_QN0meQrCGOINvdnZ0vTDrwfZyDRCmjOX3knNYYLRiCOD7J_ez5JH3OwAozhl7mJxhlGUsBs-TT9sh6E6YdLB76VJl7D4dve6btD2UTtep0k4qc0hFX6eDcEFXRqZ-L1yX2ilV_xZB2z4VprFOh7Z7nDxQwnj5ZD4vkm_v3329-ri63n7YXF1erypWoLAqaoolLlSVEywVrRRSQiFGaF4gBihFKoMUI4WKkpKqxGWWEwUwYQRhNN0ukudH3cFYz-dteI5IDCMA0ERsjkRtxY4PLs7pDtwKzf8-WNfweSAeSyhRRlVAEckKXMSSsTdRlxDXpMRR6-1cbSw7WVeyD06Yhegy0uuWN_YnzwiM89Io8GoWcPbHKH3gnfaVNEb00o7HvnMGMZn6fvEPevd0M9WIOIDulY11q0mUXzKMIEQFKSK1voOKXy07XUXrKB3fFwmvFwmRCfJXaMToPd98-fz_7Pb7kn15wrZSmNB6a8bJPH4JkiNYOet9NN_tkiHgk_NvtsEn5_PZ-THt2ekPuk26sTr-Azsc_XE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2432420020</pqid></control><display><type>article</type><title>Optimal power flow using hybrid firefly and particle swarm optimization algorithm</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Khan, Abdullah ; Hizam, Hashim ; Bin Abdul Wahab, Noor Izzri ; Lutfi Othman, Mohammad</creator><creatorcontrib>Khan, Abdullah ; Hizam, Hashim ; Bin Abdul Wahab, Noor Izzri ; Lutfi Othman, Mohammad</creatorcontrib><description>In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0235668</identifier><identifier>PMID: 32776932</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biogeography ; Biology and Life Sciences ; Computer Simulation ; Convergence ; Electric power systems ; Electricity ; Energy research ; Engineering ; Engineering and Technology ; Exploitation ; Fireflies ; Genetic algorithms ; Heuristic ; Heuristic methods ; Hybridization ; Linear programming ; Malaysia ; Management ; Methods ; Nonlinear Dynamics ; Optimization ; Optimization algorithms ; Optimization techniques ; Optimization theory ; Particle swarm optimization ; Physical Sciences ; Population ; Power flow ; Power Plants ; Reactive power ; Research and Analysis Methods ; Software ; Supervision ; Swarm intelligence ; Transmission lines ; Voltage ; Voltage stability</subject><ispartof>PloS one, 2020-08, Vol.15 (8), p.e0235668-e0235668</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Khan et al 2020 Khan et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-9d53e39fc843ef5cf2faf264589260552f71532f29b54cb3b784f034642324f03</citedby><cites>FETCH-LOGICAL-c692t-9d53e39fc843ef5cf2faf264589260552f71532f29b54cb3b784f034642324f03</cites><orcidid>0000-0003-4356-8205</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416925/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416925/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32776932$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Khan, Abdullah</creatorcontrib><creatorcontrib>Hizam, Hashim</creatorcontrib><creatorcontrib>Bin Abdul Wahab, Noor Izzri</creatorcontrib><creatorcontrib>Lutfi Othman, Mohammad</creatorcontrib><title>Optimal power flow using hybrid firefly and particle swarm optimization algorithm</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.</description><subject>Algorithms</subject><subject>Biogeography</subject><subject>Biology and Life Sciences</subject><subject>Computer Simulation</subject><subject>Convergence</subject><subject>Electric power systems</subject><subject>Electricity</subject><subject>Energy research</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Exploitation</subject><subject>Fireflies</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Heuristic methods</subject><subject>Hybridization</subject><subject>Linear programming</subject><subject>Malaysia</subject><subject>Management</subject><subject>Methods</subject><subject>Nonlinear Dynamics</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Optimization techniques</subject><subject>Optimization theory</subject><subject>Particle swarm optimization</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Power flow</subject><subject>Power Plants</subject><subject>Reactive power</subject><subject>Research and Analysis Methods</subject><subject>Software</subject><subject>Supervision</subject><subject>Swarm intelligence</subject><subject>Transmission lines</subject><subject>Voltage</subject><subject>Voltage stability</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkluL1DAYhoso7rr6D0QLgujFjDm3vRGWxcPAwuDxNqRt0mZIm5qkjuOvN3W6y1T2QnqRkO_53u_QN0meQrCGOINvdnZ0vTDrwfZyDRCmjOX3knNYYLRiCOD7J_ez5JH3OwAozhl7mJxhlGUsBs-TT9sh6E6YdLB76VJl7D4dve6btD2UTtep0k4qc0hFX6eDcEFXRqZ-L1yX2ilV_xZB2z4VprFOh7Z7nDxQwnj5ZD4vkm_v3329-ri63n7YXF1erypWoLAqaoolLlSVEywVrRRSQiFGaF4gBihFKoMUI4WKkpKqxGWWEwUwYQRhNN0ukudH3cFYz-dteI5IDCMA0ERsjkRtxY4PLs7pDtwKzf8-WNfweSAeSyhRRlVAEckKXMSSsTdRlxDXpMRR6-1cbSw7WVeyD06Yhegy0uuWN_YnzwiM89Io8GoWcPbHKH3gnfaVNEb00o7HvnMGMZn6fvEPevd0M9WIOIDulY11q0mUXzKMIEQFKSK1voOKXy07XUXrKB3fFwmvFwmRCfJXaMToPd98-fz_7Pb7kn15wrZSmNB6a8bJPH4JkiNYOet9NN_tkiHgk_NvtsEn5_PZ-THt2ekPuk26sTr-Azsc_XE</recordid><startdate>20200810</startdate><enddate>20200810</enddate><creator>Khan, Abdullah</creator><creator>Hizam, Hashim</creator><creator>Bin Abdul Wahab, Noor Izzri</creator><creator>Lutfi Othman, Mohammad</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4356-8205</orcidid></search><sort><creationdate>20200810</creationdate><title>Optimal power flow using hybrid firefly and particle swarm optimization algorithm</title><author>Khan, Abdullah ; Hizam, Hashim ; Bin Abdul Wahab, Noor Izzri ; Lutfi Othman, Mohammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-9d53e39fc843ef5cf2faf264589260552f71532f29b54cb3b784f034642324f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Biogeography</topic><topic>Biology and Life Sciences</topic><topic>Computer Simulation</topic><topic>Convergence</topic><topic>Electric power systems</topic><topic>Electricity</topic><topic>Energy research</topic><topic>Engineering</topic><topic>Engineering and Technology</topic><topic>Exploitation</topic><topic>Fireflies</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Heuristic methods</topic><topic>Hybridization</topic><topic>Linear programming</topic><topic>Malaysia</topic><topic>Management</topic><topic>Methods</topic><topic>Nonlinear Dynamics</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Optimization techniques</topic><topic>Optimization theory</topic><topic>Particle swarm optimization</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>Power flow</topic><topic>Power Plants</topic><topic>Reactive power</topic><topic>Research and Analysis Methods</topic><topic>Software</topic><topic>Supervision</topic><topic>Swarm intelligence</topic><topic>Transmission lines</topic><topic>Voltage</topic><topic>Voltage stability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khan, Abdullah</creatorcontrib><creatorcontrib>Hizam, Hashim</creatorcontrib><creatorcontrib>Bin Abdul Wahab, Noor Izzri</creatorcontrib><creatorcontrib>Lutfi Othman, Mohammad</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khan, Abdullah</au><au>Hizam, Hashim</au><au>Bin Abdul Wahab, Noor Izzri</au><au>Lutfi Othman, Mohammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal power flow using hybrid firefly and particle swarm optimization algorithm</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-08-10</date><risdate>2020</risdate><volume>15</volume><issue>8</issue><spage>e0235668</spage><epage>e0235668</epage><pages>e0235668-e0235668</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32776932</pmid><doi>10.1371/journal.pone.0235668</doi><tpages>e0235668</tpages><orcidid>https://orcid.org/0000-0003-4356-8205</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-08, Vol.15 (8), p.e0235668-e0235668 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2432420020 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Biogeography Biology and Life Sciences Computer Simulation Convergence Electric power systems Electricity Energy research Engineering Engineering and Technology Exploitation Fireflies Genetic algorithms Heuristic Heuristic methods Hybridization Linear programming Malaysia Management Methods Nonlinear Dynamics Optimization Optimization algorithms Optimization techniques Optimization theory Particle swarm optimization Physical Sciences Population Power flow Power Plants Reactive power Research and Analysis Methods Software Supervision Swarm intelligence Transmission lines Voltage Voltage stability |
| title | Optimal power flow using hybrid firefly and particle swarm optimization algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T07%3A29%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimal%20power%20flow%20using%20hybrid%20firefly%20and%20particle%20swarm%20optimization%20algorithm&rft.jtitle=PloS%20one&rft.au=Khan,%20Abdullah&rft.date=2020-08-10&rft.volume=15&rft.issue=8&rft.spage=e0235668&rft.epage=e0235668&rft.pages=e0235668-e0235668&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0235668&rft_dat=%3Cgale_plos_%3EA632112949%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2432420020&rft_id=info:pmid/32776932&rft_galeid=A632112949&rft_doaj_id=oai_doaj_org_article_54cfabf03052479394cb9fcadb13d4b3&rfr_iscdi=true |