Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization
DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying...
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| Veröffentlicht in: | Energies (Basel) 2021-11, Vol.14 (21), p.7145, Article 7145 |
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| creator | Yang, Minsheng Li, Jianqi Li, Jianying Yuan, Xiaofang Xu, Jiazhu |
| description | DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Levy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Levy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method. |
| doi_str_mv | 10.3390/en14217145 |
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Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Levy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Levy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.</description><identifier>ISSN: 1996-1073</identifier><identifier>EISSN: 1996-1073</identifier><identifier>DOI: 10.3390/en14217145</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>adaptive coding strategy ; Algorithms ; Artificial intelligence ; DC distribution network ; Efficiency ; Energy & Fuels ; fault recovery reconfiguration ; Flight ; Fractals ; Genetic algorithms ; Heuristic ; Linear programming ; Lévy flight ; Mathematical models ; Methods ; Network reliability ; Optimization ; Optimization algorithms ; particle swarm algorithm ; Reconfiguration ; Science & Technology ; Technology</subject><ispartof>Energies (Basel), 2021-11, Vol.14 (21), p.7145, Article 7145</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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>true</woscitedreferencessubscribed><woscitedreferencescount>11</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000718787300001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c361t-dd4795e2f7c3c25e4389d79101fc26179b60b309ad0bde7bf3245fb5997fffe23</citedby><cites>FETCH-LOGICAL-c361t-dd4795e2f7c3c25e4389d79101fc26179b60b309ad0bde7bf3245fb5997fffe23</cites><orcidid>0000-0001-8683-0213</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,865,2103,2115,27929,27930,39263</link.rule.ids></links><search><creatorcontrib>Yang, Minsheng</creatorcontrib><creatorcontrib>Li, Jianqi</creatorcontrib><creatorcontrib>Li, Jianying</creatorcontrib><creatorcontrib>Yuan, Xiaofang</creatorcontrib><creatorcontrib>Xu, Jiazhu</creatorcontrib><title>Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization</title><title>Energies (Basel)</title><addtitle>ENERGIES</addtitle><description>DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Levy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Levy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.</description><subject>adaptive coding strategy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>DC distribution network</subject><subject>Efficiency</subject><subject>Energy & Fuels</subject><subject>fault recovery reconfiguration</subject><subject>Flight</subject><subject>Fractals</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Linear programming</subject><subject>Lévy flight</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Network reliability</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>particle swarm algorithm</subject><subject>Reconfiguration</subject><subject>Science & Technology</subject><subject>Technology</subject><issn>1996-1073</issn><issn>1996-1073</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNUU1vEzEQtRBIVKEXfoElbqAUf-za6yNs6YdU0aqFs-WPceSQrIPtJUp_fTdJVTgylxnNvPdmNA-h95Scca7IZxhow6ikTfsKnVClxJwSyV__U79Fp6UsyRScU875CUr34NIQ4mLMpsY04Ic6FbDY4ZAyPu_xeSw1Rzseht-hblP-hS_MuKp4T_0DeYe_mgIeT_Ornc3R4zuTa3QrwA9bk9f4dlPjOj4e9N-hN8GsCpw-5xn6efHtR381v7m9vO6_3MwdF7TOvW-kaoEF6bhjLTS8U14qSmhwTFCprCCWE2U8sR6kDZw1bbCtUjKEAIzP0PVR1yez1Jsc1ybvdDJRHxopL_TzkVopr4RTKvDONTQYK5h0yjgBDaEd22t9OGptcvo9Qql6mcY8TOdr1ipBmBDTb2fo4xHlciolQ3jZSone-6P_-jOBuyN4CzaF4iIMDl4Ikz-SdrKTfG8V7WM9_K5P41An6qf_p_IntCqjiw</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Yang, Minsheng</creator><creator>Li, Jianqi</creator><creator>Li, Jianying</creator><creator>Yuan, Xiaofang</creator><creator>Xu, Jiazhu</creator><general>Mdpi</general><general>MDPI AG</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8683-0213</orcidid></search><sort><creationdate>20211101</creationdate><title>Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization</title><author>Yang, Minsheng ; Li, Jianqi ; Li, Jianying ; Yuan, Xiaofang ; Xu, Jiazhu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-dd4795e2f7c3c25e4389d79101fc26179b60b309ad0bde7bf3245fb5997fffe23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>adaptive coding strategy</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>DC distribution network</topic><topic>Efficiency</topic><topic>Energy & Fuels</topic><topic>fault recovery reconfiguration</topic><topic>Flight</topic><topic>Fractals</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Linear programming</topic><topic>Lévy flight</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Network reliability</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>particle swarm algorithm</topic><topic>Reconfiguration</topic><topic>Science & Technology</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Minsheng</creatorcontrib><creatorcontrib>Li, Jianqi</creatorcontrib><creatorcontrib>Li, Jianying</creatorcontrib><creatorcontrib>Yuan, Xiaofang</creatorcontrib><creatorcontrib>Xu, Jiazhu</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>ProQuest Central China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Energies (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Minsheng</au><au>Li, Jianqi</au><au>Li, Jianying</au><au>Yuan, Xiaofang</au><au>Xu, Jiazhu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization</atitle><jtitle>Energies (Basel)</jtitle><stitle>ENERGIES</stitle><date>2021-11-01</date><risdate>2021</risdate><volume>14</volume><issue>21</issue><spage>7145</spage><pages>7145-</pages><artnum>7145</artnum><issn>1996-1073</issn><eissn>1996-1073</eissn><abstract>DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Levy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Levy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. 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| subjects | adaptive coding strategy Algorithms Artificial intelligence DC distribution network Efficiency Energy & Fuels fault recovery reconfiguration Flight Fractals Genetic algorithms Heuristic Linear programming Lévy flight Mathematical models Methods Network reliability Optimization Optimization algorithms particle swarm algorithm Reconfiguration Science & Technology Technology |
| title | Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization |
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