Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network
Aiming at the characteristics of high target dimensionality, strong nonlinearity and complex constraints of distribution network reactive power optimization problems, this paper proposes an improved shuffled frog leaping algorithm based on SA strategy (ISFLASAS) to solve the problem. Firstly, the al...
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Veröffentlicht in: | Journal of physics. Conference series 2021-04, Vol.1865 (4), p.42056 |
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creator | Zhang, Meilun Zhang, Jin Xu, Maoda Hao, Wenbo |
description | Aiming at the characteristics of high target dimensionality, strong nonlinearity and complex constraints of distribution network reactive power optimization problems, this paper proposes an improved shuffled frog leaping algorithm based on SA strategy (ISFLASAS) to solve the problem. Firstly, the algorithm uses the global optimization capability of SFLA to detect the optimal solution, and improves the standard SFLA. Incorporating a reciprocal strategy during population initialization to improve the uniform distribution ability of the initial solution and speed up the search for the optimal solution. Secondly, using the local escape ability of the SA strategy, in the later stage of the SFLA calculation, the SA strategy is assisted to jump out of the local optimum with a certain probability to improve the global detection ability. Finally, the IEEE 33-node system is used to simulate and analyze the proposed model algorithm to verify the proposed control strategy. |
doi_str_mv | 10.1088/1742-6596/1865/4/042056 |
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Firstly, the algorithm uses the global optimization capability of SFLA to detect the optimal solution, and improves the standard SFLA. Incorporating a reciprocal strategy during population initialization to improve the uniform distribution ability of the initial solution and speed up the search for the optimal solution. Secondly, using the local escape ability of the SA strategy, in the later stage of the SFLA calculation, the SA strategy is assisted to jump out of the local optimum with a certain probability to improve the global detection ability. 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Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2436-2926b898b13b1b71da02229a58cbcc3d7cb1b0cf5527df21788cdafa17abdd743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids></links><search><creatorcontrib>Zhang, Meilun</creatorcontrib><creatorcontrib>Zhang, Jin</creatorcontrib><creatorcontrib>Xu, Maoda</creatorcontrib><creatorcontrib>Hao, Wenbo</creatorcontrib><title>Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network</title><title>Journal of physics. Conference series</title><description>Aiming at the characteristics of high target dimensionality, strong nonlinearity and complex constraints of distribution network reactive power optimization problems, this paper proposes an improved shuffled frog leaping algorithm based on SA strategy (ISFLASAS) to solve the problem. Firstly, the algorithm uses the global optimization capability of SFLA to detect the optimal solution, and improves the standard SFLA. Incorporating a reciprocal strategy during population initialization to improve the uniform distribution ability of the initial solution and speed up the search for the optimal solution. Secondly, using the local escape ability of the SA strategy, in the later stage of the SFLA calculation, the SA strategy is assisted to jump out of the local optimum with a certain probability to improve the global detection ability. Finally, the IEEE 33-node system is used to simulate and analyze the proposed model algorithm to verify the proposed control strategy.</description><subject>Algorithms</subject><subject>Electric power distribution</subject><subject>Global optimization</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Physics</subject><subject>Reactive power</subject><subject>Strategy</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNo9kF1PwjAUhhejiYj-Bpt4Pdd2H-0uFxQhQSGi103bdVjcVuw6CF75092c4Vyczzfn5Dyed4vgPYKUBohE2E_iNAkQTeIgCmCEYZyceaPT5PyUU3rpXTXNFsKwMzLyfubVzpq9ysF6ushAVm6M1e6jAofOg9lRWN2NMrB2lju1OYLCWPDclk77S7FV0um9Aq-KD8nKHJQFq5LXta43YLlzutLf3GlTA1OAB904q0X7V78odzD289q7KHjZqJv_OPbep49vk5m_WD7NJ9nClzgKEx-nOBE0pQKFAgmCcg4xximPqRRShjmRXRvKIo4xyQuMCKUy5wVHhIs8J1E49u6Gvd27X61qHNua1tbdSYZjhNKEEkI6FRlU0pqmsapgO6srbo8MQdbjZj1I1kNlPW4WsQF3-AtD3HUD</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Zhang, Meilun</creator><creator>Zhang, Jin</creator><creator>Xu, Maoda</creator><creator>Hao, Wenbo</creator><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20210401</creationdate><title>Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network</title><author>Zhang, Meilun ; Zhang, Jin ; Xu, Maoda ; Hao, Wenbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2436-2926b898b13b1b71da02229a58cbcc3d7cb1b0cf5527df21788cdafa17abdd743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Electric power distribution</topic><topic>Global optimization</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Physics</topic><topic>Reactive power</topic><topic>Strategy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Meilun</creatorcontrib><creatorcontrib>Zhang, Jin</creatorcontrib><creatorcontrib>Xu, Maoda</creatorcontrib><creatorcontrib>Hao, Wenbo</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</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><jtitle>Journal of physics. Conference series</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Meilun</au><au>Zhang, Jin</au><au>Xu, Maoda</au><au>Hao, Wenbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network</atitle><jtitle>Journal of physics. Conference series</jtitle><date>2021-04-01</date><risdate>2021</risdate><volume>1865</volume><issue>4</issue><spage>42056</spage><pages>42056-</pages><issn>1742-6588</issn><eissn>1742-6596</eissn><abstract>Aiming at the characteristics of high target dimensionality, strong nonlinearity and complex constraints of distribution network reactive power optimization problems, this paper proposes an improved shuffled frog leaping algorithm based on SA strategy (ISFLASAS) to solve the problem. Firstly, the algorithm uses the global optimization capability of SFLA to detect the optimal solution, and improves the standard SFLA. Incorporating a reciprocal strategy during population initialization to improve the uniform distribution ability of the initial solution and speed up the search for the optimal solution. Secondly, using the local escape ability of the SA strategy, in the later stage of the SFLA calculation, the SA strategy is assisted to jump out of the local optimum with a certain probability to improve the global detection ability. Finally, the IEEE 33-node system is used to simulate and analyze the proposed model algorithm to verify the proposed control strategy.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1742-6596/1865/4/042056</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Electric power distribution Global optimization Multiple objective analysis Optimization Physics Reactive power Strategy |
title | Improved SFLA Algorithm with Hybrid SA Strategy for Multi-Objective Reactive Power Planning Optimization of Distribution Network |
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