Wind turbine generator set blade-pitch variation safety prediction algorithm
In a wind turbine generator set blade-pitch variation safety prediction algorithm, the icing possibility of blades is predicted through a BP (back-propagation) three-layered neural network. The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input la...
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| creator | LI NAN MA JINGCONG JIAO BIN |
| description | In a wind turbine generator set blade-pitch variation safety prediction algorithm, the icing possibility of blades is predicted through a BP (back-propagation) three-layered neural network. The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input layer comprises eight input nodes including the wind speed x1, the air temperature x2, the air humidity x3, the blade-pitch variation angle x4, the impeller rotation speed x5, the blade spatial location x6, the fan vibration value x7 and the blade material coefficient x8; the hidden layer comprises three nodes of z1, z2 and z3; the output layer comprises one output node, namely the icing speed v. By the wind turbine generator set blade-pitch variation safety prediction algorithm, the service life of blades of the wind turbine generator set is prolonged and the power generation efficiency of the wind turbine generator set is improved. |
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The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input layer comprises eight input nodes including the wind speed x1, the air temperature x2, the air humidity x3, the blade-pitch variation angle x4, the impeller rotation speed x5, the blade spatial location x6, the fan vibration value x7 and the blade material coefficient x8; the hidden layer comprises three nodes of z1, z2 and z3; the output layer comprises one output node, namely the icing speed v. By the wind turbine generator set blade-pitch variation safety prediction algorithm, the service life of blades of the wind turbine generator set is prolonged and the power generation efficiency of the wind turbine generator set is improved.</description><language>chi ; eng</language><subject>BLASTING ; HEATING ; LIGHTING ; MACHINES OR ENGINES FOR LIQUIDS ; MECHANICAL ENGINEERING ; OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR ; PRODUCING MECHANICAL POWER ; WEAPONS ; WIND MOTORS ; WIND, SPRING WEIGHT AND MISCELLANEOUS MOTORS</subject><creationdate>2014</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20140226&DB=EPODOC&CC=CN&NR=103603776A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20140226&DB=EPODOC&CC=CN&NR=103603776A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI NAN</creatorcontrib><creatorcontrib>MA JINGCONG</creatorcontrib><creatorcontrib>JIAO BIN</creatorcontrib><title>Wind turbine generator set blade-pitch variation safety prediction algorithm</title><description>In a wind turbine generator set blade-pitch variation safety prediction algorithm, the icing possibility of blades is predicted through a BP (back-propagation) three-layered neural network. The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input layer comprises eight input nodes including the wind speed x1, the air temperature x2, the air humidity x3, the blade-pitch variation angle x4, the impeller rotation speed x5, the blade spatial location x6, the fan vibration value x7 and the blade material coefficient x8; the hidden layer comprises three nodes of z1, z2 and z3; the output layer comprises one output node, namely the icing speed v. By the wind turbine generator set blade-pitch variation safety prediction algorithm, the service life of blades of the wind turbine generator set is prolonged and the power generation efficiency of the wind turbine generator set is improved.</description><subject>BLASTING</subject><subject>HEATING</subject><subject>LIGHTING</subject><subject>MACHINES OR ENGINES FOR LIQUIDS</subject><subject>MECHANICAL ENGINEERING</subject><subject>OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR</subject><subject>PRODUCING MECHANICAL POWER</subject><subject>WEAPONS</subject><subject>WIND MOTORS</subject><subject>WIND, SPRING WEIGHT AND MISCELLANEOUS MOTORS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2014</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPAJz8xLUSgpLUrKzEtVSE_NSy1KLMkvUihOLVFIyklMSdUtyCxJzlAoSyzKTCzJzM9TKE5MSy2pVCgoSk3JTAaLJOak5xdllmTk8jCwpiXmFKfyQmluBkU31xBnD93Ugvz41OKCxGSg-SXxzn6GBsZmBsbm5maOxsSoAQDXVTZh</recordid><startdate>20140226</startdate><enddate>20140226</enddate><creator>LI NAN</creator><creator>MA JINGCONG</creator><creator>JIAO BIN</creator><scope>EVB</scope></search><sort><creationdate>20140226</creationdate><title>Wind turbine generator set blade-pitch variation safety prediction algorithm</title><author>LI NAN ; MA JINGCONG ; JIAO BIN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN103603776A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2014</creationdate><topic>BLASTING</topic><topic>HEATING</topic><topic>LIGHTING</topic><topic>MACHINES OR ENGINES FOR LIQUIDS</topic><topic>MECHANICAL ENGINEERING</topic><topic>OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR</topic><topic>PRODUCING MECHANICAL POWER</topic><topic>WEAPONS</topic><topic>WIND MOTORS</topic><topic>WIND, SPRING WEIGHT AND MISCELLANEOUS MOTORS</topic><toplevel>online_resources</toplevel><creatorcontrib>LI NAN</creatorcontrib><creatorcontrib>MA JINGCONG</creatorcontrib><creatorcontrib>JIAO BIN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI NAN</au><au>MA JINGCONG</au><au>JIAO BIN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Wind turbine generator set blade-pitch variation safety prediction algorithm</title><date>2014-02-26</date><risdate>2014</risdate><abstract>In a wind turbine generator set blade-pitch variation safety prediction algorithm, the icing possibility of blades is predicted through a BP (back-propagation) three-layered neural network. The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input layer comprises eight input nodes including the wind speed x1, the air temperature x2, the air humidity x3, the blade-pitch variation angle x4, the impeller rotation speed x5, the blade spatial location x6, the fan vibration value x7 and the blade material coefficient x8; the hidden layer comprises three nodes of z1, z2 and z3; the output layer comprises one output node, namely the icing speed v. By the wind turbine generator set blade-pitch variation safety prediction algorithm, the service life of blades of the wind turbine generator set is prolonged and the power generation efficiency of the wind turbine generator set is improved.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | BLASTING HEATING LIGHTING MACHINES OR ENGINES FOR LIQUIDS MECHANICAL ENGINEERING OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR PRODUCING MECHANICAL POWER WEAPONS WIND MOTORS WIND, SPRING WEIGHT AND MISCELLANEOUS MOTORS |
| title | Wind turbine generator set blade-pitch variation safety prediction algorithm |
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