Neural Network and Nonlinear Prime-Dual Interior Algorithm for Optimization Reactive Power Compensation
This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algo...
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| creator | Lu Fang An Luo Xianyong Xu Houhui Fang |
| description | This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the problems that the electric power has complicated parameters and it is hard to constitute the compensation system model. The result shows that the effect of this intelligent control system is good and this algorithm is valid. |
| doi_str_mv | 10.1109/iCECE.2010.951 |
| format | Conference Proceeding |
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The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the problems that the electric power has complicated parameters and it is hard to constitute the compensation system model. The result shows that the effect of this intelligent control system is good and this algorithm is valid.</description><identifier>ISBN: 1424468809</identifier><identifier>ISBN: 9781424468805</identifier><identifier>EISBN: 9781424468812</identifier><identifier>EISBN: 0769540317</identifier><identifier>EISBN: 9780769540313</identifier><identifier>EISBN: 1424468817</identifier><identifier>DOI: 10.1109/iCECE.2010.951</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Mathematical model ; neural network ; Neurons ; nonlinear prime-dual interior algorithm ; Optimization ; Prediction algorithms ; Reactive power ; reactive power optimization compensation ; static var compensator</subject><ispartof>2010 International Conference on Electrical and Control Engineering, 2010, p.3898-3901</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5629541$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5629541$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lu Fang</creatorcontrib><creatorcontrib>An Luo</creatorcontrib><creatorcontrib>Xianyong Xu</creatorcontrib><creatorcontrib>Houhui Fang</creatorcontrib><title>Neural Network and Nonlinear Prime-Dual Interior Algorithm for Optimization Reactive Power Compensation</title><title>2010 International Conference on Electrical and Control Engineering</title><addtitle>ICECE</addtitle><description>This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the problems that the electric power has complicated parameters and it is hard to constitute the compensation system model. The result shows that the effect of this intelligent control system is good and this algorithm is valid.</description><subject>Artificial neural networks</subject><subject>Mathematical model</subject><subject>neural network</subject><subject>Neurons</subject><subject>nonlinear prime-dual interior algorithm</subject><subject>Optimization</subject><subject>Prediction algorithms</subject><subject>Reactive power</subject><subject>reactive power optimization compensation</subject><subject>static var compensator</subject><isbn>1424468809</isbn><isbn>9781424468805</isbn><isbn>9781424468812</isbn><isbn>0769540317</isbn><isbn>9780769540313</isbn><isbn>1424468817</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j8tOwzAURI0QElCyZcPGP5DiZ2IvqxBKpSqtUPeVa66LIYkrx6WCryfiMZvR0Yyu7iB0S8mUUqLvfVVX9ZSRkbWkZyjTpaKCCVEoRdk5uv4Hoi9RNgxvZJSQjFF9hfYNHKNpcQPpFOI7Nv0LbkLf-h5MxOvoO8gfjmNh0SeIPkQ8a_ch-vTaYTfS6pB8579M8qHHz2Bs8h-A1-EEEVehO0A__GQ36MKZdoDszydo81hvqqd8uZovqtky95qkHKxlwEsuwBnnLBeCkVJqKpXgYKW0BRgw3ChLnKRcSSPHydTZ3a5QjAo-QXe_Zz0AbA_j-yZ-bmXBtBSUfwODJliN</recordid><startdate>201006</startdate><enddate>201006</enddate><creator>Lu Fang</creator><creator>An Luo</creator><creator>Xianyong Xu</creator><creator>Houhui Fang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201006</creationdate><title>Neural Network and Nonlinear Prime-Dual Interior Algorithm for Optimization Reactive Power Compensation</title><author>Lu Fang ; An Luo ; Xianyong Xu ; Houhui Fang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-ecc2e3734efaffc3442075915843ec55c6eaea3a8c0f51385a59511fcbb682143</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Mathematical model</topic><topic>neural network</topic><topic>Neurons</topic><topic>nonlinear prime-dual interior algorithm</topic><topic>Optimization</topic><topic>Prediction algorithms</topic><topic>Reactive power</topic><topic>reactive power optimization compensation</topic><topic>static var compensator</topic><toplevel>online_resources</toplevel><creatorcontrib>Lu Fang</creatorcontrib><creatorcontrib>An Luo</creatorcontrib><creatorcontrib>Xianyong Xu</creatorcontrib><creatorcontrib>Houhui Fang</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 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>Lu Fang</au><au>An Luo</au><au>Xianyong Xu</au><au>Houhui Fang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural Network and Nonlinear Prime-Dual Interior Algorithm for Optimization Reactive Power Compensation</atitle><btitle>2010 International Conference on Electrical and Control Engineering</btitle><stitle>ICECE</stitle><date>2010-06</date><risdate>2010</risdate><spage>3898</spage><epage>3901</epage><pages>3898-3901</pages><isbn>1424468809</isbn><isbn>9781424468805</isbn><eisbn>9781424468812</eisbn><eisbn>0769540317</eisbn><eisbn>9780769540313</eisbn><eisbn>1424468817</eisbn><abstract>This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the problems that the electric power has complicated parameters and it is hard to constitute the compensation system model. The result shows that the effect of this intelligent control system is good and this algorithm is valid.</abstract><pub>IEEE</pub><doi>10.1109/iCECE.2010.951</doi><tpages>4</tpages></addata></record> |
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| subjects | Artificial neural networks Mathematical model neural network Neurons nonlinear prime-dual interior algorithm Optimization Prediction algorithms Reactive power reactive power optimization compensation static var compensator |
| title | Neural Network and Nonlinear Prime-Dual Interior Algorithm for Optimization Reactive Power Compensation |
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