Application research based on improved genetic algorithm for optimum design of power transformers
In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genet...
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| creator | Li Hui Han Li He Bei Yang Shunchang |
| description | In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design. |
| doi_str_mv | 10.1109/ICEMS.2001.970657 |
| format | Conference Proceeding |
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An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.</description><identifier>ISBN: 7506251159</identifier><identifier>ISBN: 9787506251150</identifier><identifier>DOI: 10.1109/ICEMS.2001.970657</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Biological cells ; Cost function ; Design optimization ; Educational institutions ; Encoding ; Genetic algorithms ; Helium ; Power engineering and energy ; Power transformers</subject><ispartof>ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. 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Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)</title><addtitle>ICEMS</addtitle><description>In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.</description><subject>Algorithm design and analysis</subject><subject>Biological cells</subject><subject>Cost function</subject><subject>Design optimization</subject><subject>Educational institutions</subject><subject>Encoding</subject><subject>Genetic algorithms</subject><subject>Helium</subject><subject>Power engineering and energy</subject><subject>Power transformers</subject><isbn>7506251159</isbn><isbn>9787506251150</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkM1KxDAcxAMiqOs-gJ7yAq35aNLmuCyrLqx4UM9Lkv7bjTRNSKLi21tY5zLDj2EOg9AdJTWlRD3st7uXt5oRQmvVEinaC3TTCiKZoFSoK7TO-ZMs4qrhor1GehPj5KwuLsw4QQad7AkbnaHHC3E-pvC95BFmKM5iPY0huXLyeAgJh1ic__K4h-zGGYcBx_ADCZek57wUPKR8iy4HPWVY__sKfTzu3rfP1eH1ab_dHCrLGCuV5o1lnNtuUKwnWhkmZGeMpmCNaSnhVjVysBIaYRtFiFHCEK77TkjGqRR8he7Puw4AjjE5r9Pv8XwC_wMG71Ro</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Li Hui</creator><creator>Han Li</creator><creator>He Bei</creator><creator>Yang Shunchang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2001</creationdate><title>Application research based on improved genetic algorithm for optimum design of power transformers</title><author>Li Hui ; Han Li ; He Bei ; Yang Shunchang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c222t-a34c233c8f92d0a9b2568bba1ecbb7103c946fc6e45c4900b95b03ad856231653</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Algorithm design and analysis</topic><topic>Biological cells</topic><topic>Cost function</topic><topic>Design optimization</topic><topic>Educational institutions</topic><topic>Encoding</topic><topic>Genetic algorithms</topic><topic>Helium</topic><topic>Power engineering and energy</topic><topic>Power transformers</topic><toplevel>online_resources</toplevel><creatorcontrib>Li Hui</creatorcontrib><creatorcontrib>Han Li</creatorcontrib><creatorcontrib>He Bei</creatorcontrib><creatorcontrib>Yang Shunchang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Hui</au><au>Han Li</au><au>He Bei</au><au>Yang Shunchang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application research based on improved genetic algorithm for optimum design of power transformers</atitle><btitle>ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)</btitle><stitle>ICEMS</stitle><date>2001</date><risdate>2001</risdate><volume>1</volume><spage>242</spage><epage>245 vol.1</epage><pages>242-245 vol.1</pages><isbn>7506251159</isbn><isbn>9787506251150</isbn><abstract>In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.</abstract><pub>IEEE</pub><doi>10.1109/ICEMS.2001.970657</doi></addata></record> |
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| ispartof | ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501), 2001, Vol.1, p.242-245 vol.1 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Biological cells Cost function Design optimization Educational institutions Encoding Genetic algorithms Helium Power engineering and energy Power transformers |
| title | Application research based on improved genetic algorithm for optimum design of power transformers |
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