Intelligent distribution network fault diagnosis positioning method based on deep learning algorithm
According to the intelligent distribution network fault diagnosis and positioning method based on the deep learning algorithm, the problems that power distribution network fault diagnosis and positioning time is long, fault point positioning is not accurate enough, and the first-aid repair efficienc...
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| creator | WANG LEI BAI JINGTAO ZHANG HAIRANG LI SASA ZHU YUE JIE YEWEI LIU YUNHUI ZANG ZHI CAO RUI PENG BIN XU JIAN MA ZHENQI WANG TENGDA WANG ZEWEN XU DONGQIN WU MINGCHAO LI YUHAO LIU SU TANG JIAHAN ZHENG MINGZHOU |
| description | According to the intelligent distribution network fault diagnosis and positioning method based on the deep learning algorithm, the problems that power distribution network fault diagnosis and positioning time is long, fault point positioning is not accurate enough, and the first-aid repair efficiency of first-aid repair personnel is low are solved, and the intelligent distribution network fault diagnosis and positioning method based on the deep learning algorithm is provided. According to the method, data preprocessing is performed on fault data, abnormal data and missing data are eliminated, and recognition of the fault type of the power distribution network is completed by picking up a fault recognition model; an intelligent fault line selection model of the power distribution network is graded based on a deep learning algorithm, and fault lines are screened according to the model; and finally, visually displaying the fault section and the fault point of the power distribution network in the positioned faul |
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According to the method, data preprocessing is performed on fault data, abnormal data and missing data are eliminated, and recognition of the fault type of the power distribution network is completed by picking up a fault recognition model; an intelligent fault line selection model of the power distribution network is graded based on a deep learning algorithm, and fault lines are screened according to the model; and finally, visually displaying the fault section and the fault point of the power distribution network in the positioned faul</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; MEASURING ; MEASURING ELECTRIC VARIABLES ; MEASURING MAGNETIC VARIABLES ; PHYSICS ; TESTING</subject><creationdate>2024</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=20240503&DB=EPODOC&CC=CN&NR=117970020A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240503&DB=EPODOC&CC=CN&NR=117970020A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG LEI</creatorcontrib><creatorcontrib>BAI JINGTAO</creatorcontrib><creatorcontrib>ZHANG HAIRANG</creatorcontrib><creatorcontrib>LI SASA</creatorcontrib><creatorcontrib>ZHU YUE</creatorcontrib><creatorcontrib>JIE YEWEI</creatorcontrib><creatorcontrib>LIU YUNHUI</creatorcontrib><creatorcontrib>ZANG ZHI</creatorcontrib><creatorcontrib>CAO RUI</creatorcontrib><creatorcontrib>PENG BIN</creatorcontrib><creatorcontrib>XU JIAN</creatorcontrib><creatorcontrib>MA ZHENQI</creatorcontrib><creatorcontrib>WANG TENGDA</creatorcontrib><creatorcontrib>WANG ZEWEN</creatorcontrib><creatorcontrib>XU DONGQIN</creatorcontrib><creatorcontrib>WU MINGCHAO</creatorcontrib><creatorcontrib>LI YUHAO</creatorcontrib><creatorcontrib>LIU SU</creatorcontrib><creatorcontrib>TANG JIAHAN</creatorcontrib><creatorcontrib>ZHENG MINGZHOU</creatorcontrib><title>Intelligent distribution network fault diagnosis positioning method based on deep learning algorithm</title><description>According to the intelligent distribution network fault diagnosis and positioning method based on the deep learning algorithm, the problems that power distribution network fault diagnosis and positioning time is long, fault point positioning is not accurate enough, and the first-aid repair efficiency of first-aid repair personnel is low are solved, and the intelligent distribution network fault diagnosis and positioning method based on the deep learning algorithm is provided. 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According to the method, data preprocessing is performed on fault data, abnormal data and missing data are eliminated, and recognition of the fault type of the power distribution network is completed by picking up a fault recognition model; an intelligent fault line selection model of the power distribution network is graded based on a deep learning algorithm, and fault lines are screened according to the model; and finally, visually displaying the fault section and the fault point of the power distribution network in the positioned faul</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING ELECTRIC VARIABLES MEASURING MAGNETIC VARIABLES PHYSICS TESTING |
| title | Intelligent distribution network fault diagnosis positioning method based on deep learning algorithm |
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