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A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization
The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the...
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Published in: | Energy (Oxford) 2019-03, Vol.171, p.218-240 |
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creator | Nguyen, Thang Trung |
description | The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the paper by performing three modifications with intent to improve optimal solution quality and speed up convergence of conventional social spider optimization (SSO). The first and the second modifications are to focus on new solution generation by changing the movement strategy of female spiders and male spiders while the third modification is to fix the female spider rate to an appropriate ratio. The performance of the proposed method is evaluated by testing on three IEEE systems with 30, 57 and 118 buses. As a result, the proposed method has advantages over SSO such as simpler application, fewer number of control parameters, spend less time tuning control parameter values, faster convergence to optimal solutions and more stable search ability. In addition, the proposed method's results are also compared to other existing methods and the indications are that the proposed method can find better optimal solutions, use lower number of generated solutions and faster convergence.
•Novel Improved Social Spider Optimization (NISSO) is first proposed in the paper.•Optimal power flow (OPF) problem is one of the most complicated problems.•NISSO is executed for OPF problem with three IEEE systems with 30, 57 and 118 buses.•NISSO can find high quality solutions of OPF problem with fast search ability.•NISSO is superior to approximately all compared methods for all study cases. |
doi_str_mv | 10.1016/j.energy.2019.01.021 |
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•Novel Improved Social Spider Optimization (NISSO) is first proposed in the paper.•Optimal power flow (OPF) problem is one of the most complicated problems.•NISSO is executed for OPF problem with three IEEE systems with 30, 57 and 118 buses.•NISSO can find high quality solutions of OPF problem with fast search ability.•NISSO is superior to approximately all compared methods for all study cases.</description><identifier>ISSN: 0360-5442</identifier><identifier>EISSN: 1873-6785</identifier><identifier>DOI: 10.1016/j.energy.2019.01.021</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Air pollution ; Algorithms ; Convergence ; Convergence speed ; Electric power loss ; Electricity pricing ; Fitness function ; IEEE power systems ; Optimal power flow ; Optimization ; Optimization algorithms ; Parameters ; Power flow ; Social spider optimization ; Spiders ; Test procedures ; Transmission power network</subject><ispartof>Energy (Oxford), 2019-03, Vol.171, p.218-240</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Mar 15, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-8cc4e5dc173a68f3bae693122182a5d6612839cf9cbf8fa94c17e4e2f10c30c43</citedby></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>Nguyen, Thang Trung</creatorcontrib><title>A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization</title><title>Energy (Oxford)</title><description>The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the paper by performing three modifications with intent to improve optimal solution quality and speed up convergence of conventional social spider optimization (SSO). The first and the second modifications are to focus on new solution generation by changing the movement strategy of female spiders and male spiders while the third modification is to fix the female spider rate to an appropriate ratio. The performance of the proposed method is evaluated by testing on three IEEE systems with 30, 57 and 118 buses. As a result, the proposed method has advantages over SSO such as simpler application, fewer number of control parameters, spend less time tuning control parameter values, faster convergence to optimal solutions and more stable search ability. In addition, the proposed method's results are also compared to other existing methods and the indications are that the proposed method can find better optimal solutions, use lower number of generated solutions and faster convergence.
•Novel Improved Social Spider Optimization (NISSO) is first proposed in the paper.•Optimal power flow (OPF) problem is one of the most complicated problems.•NISSO is executed for OPF problem with three IEEE systems with 30, 57 and 118 buses.•NISSO can find high quality solutions of OPF problem with fast search ability.•NISSO is superior to approximately all compared methods for all study cases.</description><subject>Air pollution</subject><subject>Algorithms</subject><subject>Convergence</subject><subject>Convergence speed</subject><subject>Electric power loss</subject><subject>Electricity pricing</subject><subject>Fitness function</subject><subject>IEEE power systems</subject><subject>Optimal power flow</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Parameters</subject><subject>Power flow</subject><subject>Social spider optimization</subject><subject>Spiders</subject><subject>Test procedures</subject><subject>Transmission power network</subject><issn>0360-5442</issn><issn>1873-6785</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPwzAUhS0EEqXwDxgsMSf4kYezIFUVLwmJBWbLda5TR2kc7LRV--txCQsLk4f7nWOdD6FbSlJKaHHfptCDbw4pI7RKCU0Jo2doRkXJk6IU-TmaEV6QJM8ydomuQmgJIbmoqhk6LvDaNms8gDfOb1SvAQenrepwGGwNHrthtBt7VKN1PVZd47wd1xsc6ekUycHtI2g6t4_ZbvtD7iOFg-2bDrBbtaBHu4M_ZdfowqguwM3vO0efT48fy5fk7f35dbl4SzQX5ZgIrTPIa01Lrgph-EpBUXHKGBVM5XVRUCZ4pU2lV0YYVWWRhAyYoURzojM-R3dT7-Dd1xbCKFu39X38UjJGSpoVeUkilU2U9i4ED0YOPo7zB0mJPFmWrZwsy5NlSaiMlmPsYYpBXLCz4GXQFqLF2vq4WdbO_l_wDcGtiys</recordid><startdate>20190315</startdate><enddate>20190315</enddate><creator>Nguyen, Thang Trung</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20190315</creationdate><title>A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization</title><author>Nguyen, Thang Trung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-8cc4e5dc173a68f3bae693122182a5d6612839cf9cbf8fa94c17e4e2f10c30c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Air pollution</topic><topic>Algorithms</topic><topic>Convergence</topic><topic>Convergence speed</topic><topic>Electric power loss</topic><topic>Electricity pricing</topic><topic>Fitness function</topic><topic>IEEE power systems</topic><topic>Optimal power flow</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Parameters</topic><topic>Power flow</topic><topic>Social spider optimization</topic><topic>Spiders</topic><topic>Test procedures</topic><topic>Transmission power network</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen, Thang Trung</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen, Thang Trung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization</atitle><jtitle>Energy (Oxford)</jtitle><date>2019-03-15</date><risdate>2019</risdate><volume>171</volume><spage>218</spage><epage>240</epage><pages>218-240</pages><issn>0360-5442</issn><eissn>1873-6785</eissn><abstract>The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the paper by performing three modifications with intent to improve optimal solution quality and speed up convergence of conventional social spider optimization (SSO). The first and the second modifications are to focus on new solution generation by changing the movement strategy of female spiders and male spiders while the third modification is to fix the female spider rate to an appropriate ratio. The performance of the proposed method is evaluated by testing on three IEEE systems with 30, 57 and 118 buses. As a result, the proposed method has advantages over SSO such as simpler application, fewer number of control parameters, spend less time tuning control parameter values, faster convergence to optimal solutions and more stable search ability. In addition, the proposed method's results are also compared to other existing methods and the indications are that the proposed method can find better optimal solutions, use lower number of generated solutions and faster convergence.
•Novel Improved Social Spider Optimization (NISSO) is first proposed in the paper.•Optimal power flow (OPF) problem is one of the most complicated problems.•NISSO is executed for OPF problem with three IEEE systems with 30, 57 and 118 buses.•NISSO can find high quality solutions of OPF problem with fast search ability.•NISSO is superior to approximately all compared methods for all study cases.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2019.01.021</doi><tpages>23</tpages></addata></record> |
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source | ScienceDirect Freedom Collection 2022-2024 |
subjects | Air pollution Algorithms Convergence Convergence speed Electric power loss Electricity pricing Fitness function IEEE power systems Optimal power flow Optimization Optimization algorithms Parameters Power flow Social spider optimization Spiders Test procedures Transmission power network |
title | A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization |
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