Oil-immersed transformer fault diagnosis method based on multi-source data fusion
The invention belongs to the technical field of transformer fault diagnosis, and relates to an oil-immersed transformer fault diagnosis method based on multi-source data fusion, which comprises the following steps of: selecting five gases as indexes for analyzing gas dissolved in transformer oil, a...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | LUO LANG PAN XIAOLU LYU JIAWEI TANG WEI DING GUILI ZHANG ZIXI KANG BING TONG XIN XU ZHIHAO WU DIWEI HE JIAHUI DENG HUAPU HOU CHENG WU XIAORUI LI JIA YANG FENGFAN GAO MUFENG HE QI ZHANG LU WANG ZONGYAO ZHAO ZEYU WANG YONGJIE DU JUN |
description | The invention belongs to the technical field of transformer fault diagnosis, and relates to an oil-immersed transformer fault diagnosis method based on multi-source data fusion, which comprises the following steps of: selecting five gases as indexes for analyzing gas dissolved in transformer oil, a winding direct-current resistance unbalance coefficient, a winding absorption ratio, a polarization index, an iron core grounding current and a micro-water content; constructing a multi-source information fusion index; the multi-source information fusion indexes and the corresponding transformer fault types form a data set, and the data set is input into an SVM network for training; kernel function parameters and penalty parameters are optimized by adopting a zebra algorithm, and the established ZOA-SVM transformer fault diagnosis model is used for transformer fault diagnosis. According to the method, various state information amounts are analyzed through multi-source information fusion of the transformer, the SVM |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117786516A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117786516A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117786516A3</originalsourceid><addsrcrecordid>eNqNikEKwjAQAHvxIOof1gfkEMTWqxTFkyJ4L2uz0YUkW7LJ_63gAzwNw8yyud84GI6RspKDkjGplzwreKyhgGN8JVFWiFTe4uCJ31ESxDmzUal5JHBYEHxVlrRuFh6D0ubHVbM9nx79xdAkA-mEIyUqQ3-1tusO7d62x90_zwcK-zgX</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Oil-immersed transformer fault diagnosis method based on multi-source data fusion</title><source>esp@cenet</source><creator>LUO LANG ; PAN XIAOLU ; LYU JIAWEI ; TANG WEI ; DING GUILI ; ZHANG ZIXI ; KANG BING ; TONG XIN ; XU ZHIHAO ; WU DIWEI ; HE JIAHUI ; DENG HUAPU ; HOU CHENG ; WU XIAORUI ; LI JIA ; YANG FENGFAN ; GAO MUFENG ; HE QI ; ZHANG LU ; WANG ZONGYAO ; ZHAO ZEYU ; WANG YONGJIE ; DU JUN</creator><creatorcontrib>LUO LANG ; PAN XIAOLU ; LYU JIAWEI ; TANG WEI ; DING GUILI ; ZHANG ZIXI ; KANG BING ; TONG XIN ; XU ZHIHAO ; WU DIWEI ; HE JIAHUI ; DENG HUAPU ; HOU CHENG ; WU XIAORUI ; LI JIA ; YANG FENGFAN ; GAO MUFENG ; HE QI ; ZHANG LU ; WANG ZONGYAO ; ZHAO ZEYU ; WANG YONGJIE ; DU JUN</creatorcontrib><description>The invention belongs to the technical field of transformer fault diagnosis, and relates to an oil-immersed transformer fault diagnosis method based on multi-source data fusion, which comprises the following steps of: selecting five gases as indexes for analyzing gas dissolved in transformer oil, a winding direct-current resistance unbalance coefficient, a winding absorption ratio, a polarization index, an iron core grounding current and a micro-water content; constructing a multi-source information fusion index; the multi-source information fusion indexes and the corresponding transformer fault types form a data set, and the data set is input into an SVM network for training; kernel function parameters and penalty parameters are optimized by adopting a zebra algorithm, and the established ZOA-SVM transformer fault diagnosis model is used for transformer fault diagnosis. According to the method, various state information amounts are analyzed through multi-source information fusion of the transformer, the SVM</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</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=20240329&DB=EPODOC&CC=CN&NR=117786516A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240329&DB=EPODOC&CC=CN&NR=117786516A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LUO LANG</creatorcontrib><creatorcontrib>PAN XIAOLU</creatorcontrib><creatorcontrib>LYU JIAWEI</creatorcontrib><creatorcontrib>TANG WEI</creatorcontrib><creatorcontrib>DING GUILI</creatorcontrib><creatorcontrib>ZHANG ZIXI</creatorcontrib><creatorcontrib>KANG BING</creatorcontrib><creatorcontrib>TONG XIN</creatorcontrib><creatorcontrib>XU ZHIHAO</creatorcontrib><creatorcontrib>WU DIWEI</creatorcontrib><creatorcontrib>HE JIAHUI</creatorcontrib><creatorcontrib>DENG HUAPU</creatorcontrib><creatorcontrib>HOU CHENG</creatorcontrib><creatorcontrib>WU XIAORUI</creatorcontrib><creatorcontrib>LI JIA</creatorcontrib><creatorcontrib>YANG FENGFAN</creatorcontrib><creatorcontrib>GAO MUFENG</creatorcontrib><creatorcontrib>HE QI</creatorcontrib><creatorcontrib>ZHANG LU</creatorcontrib><creatorcontrib>WANG ZONGYAO</creatorcontrib><creatorcontrib>ZHAO ZEYU</creatorcontrib><creatorcontrib>WANG YONGJIE</creatorcontrib><creatorcontrib>DU JUN</creatorcontrib><title>Oil-immersed transformer fault diagnosis method based on multi-source data fusion</title><description>The invention belongs to the technical field of transformer fault diagnosis, and relates to an oil-immersed transformer fault diagnosis method based on multi-source data fusion, which comprises the following steps of: selecting five gases as indexes for analyzing gas dissolved in transformer oil, a winding direct-current resistance unbalance coefficient, a winding absorption ratio, a polarization index, an iron core grounding current and a micro-water content; constructing a multi-source information fusion index; the multi-source information fusion indexes and the corresponding transformer fault types form a data set, and the data set is input into an SVM network for training; kernel function parameters and penalty parameters are optimized by adopting a zebra algorithm, and the established ZOA-SVM transformer fault diagnosis model is used for transformer fault diagnosis. According to the method, various state information amounts are analyzed through multi-source information fusion of the transformer, the SVM</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNikEKwjAQAHvxIOof1gfkEMTWqxTFkyJ4L2uz0YUkW7LJ_63gAzwNw8yyud84GI6RspKDkjGplzwreKyhgGN8JVFWiFTe4uCJ31ESxDmzUal5JHBYEHxVlrRuFh6D0ubHVbM9nx79xdAkA-mEIyUqQ3-1tusO7d62x90_zwcK-zgX</recordid><startdate>20240329</startdate><enddate>20240329</enddate><creator>LUO LANG</creator><creator>PAN XIAOLU</creator><creator>LYU JIAWEI</creator><creator>TANG WEI</creator><creator>DING GUILI</creator><creator>ZHANG ZIXI</creator><creator>KANG BING</creator><creator>TONG XIN</creator><creator>XU ZHIHAO</creator><creator>WU DIWEI</creator><creator>HE JIAHUI</creator><creator>DENG HUAPU</creator><creator>HOU CHENG</creator><creator>WU XIAORUI</creator><creator>LI JIA</creator><creator>YANG FENGFAN</creator><creator>GAO MUFENG</creator><creator>HE QI</creator><creator>ZHANG LU</creator><creator>WANG ZONGYAO</creator><creator>ZHAO ZEYU</creator><creator>WANG YONGJIE</creator><creator>DU JUN</creator><scope>EVB</scope></search><sort><creationdate>20240329</creationdate><title>Oil-immersed transformer fault diagnosis method based on multi-source data fusion</title><author>LUO LANG ; PAN XIAOLU ; LYU JIAWEI ; TANG WEI ; DING GUILI ; ZHANG ZIXI ; KANG BING ; TONG XIN ; XU ZHIHAO ; WU DIWEI ; HE JIAHUI ; DENG HUAPU ; HOU CHENG ; WU XIAORUI ; LI JIA ; YANG FENGFAN ; GAO MUFENG ; HE QI ; ZHANG LU ; WANG ZONGYAO ; ZHAO ZEYU ; WANG YONGJIE ; DU JUN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117786516A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LUO LANG</creatorcontrib><creatorcontrib>PAN XIAOLU</creatorcontrib><creatorcontrib>LYU JIAWEI</creatorcontrib><creatorcontrib>TANG WEI</creatorcontrib><creatorcontrib>DING GUILI</creatorcontrib><creatorcontrib>ZHANG ZIXI</creatorcontrib><creatorcontrib>KANG BING</creatorcontrib><creatorcontrib>TONG XIN</creatorcontrib><creatorcontrib>XU ZHIHAO</creatorcontrib><creatorcontrib>WU DIWEI</creatorcontrib><creatorcontrib>HE JIAHUI</creatorcontrib><creatorcontrib>DENG HUAPU</creatorcontrib><creatorcontrib>HOU CHENG</creatorcontrib><creatorcontrib>WU XIAORUI</creatorcontrib><creatorcontrib>LI JIA</creatorcontrib><creatorcontrib>YANG FENGFAN</creatorcontrib><creatorcontrib>GAO MUFENG</creatorcontrib><creatorcontrib>HE QI</creatorcontrib><creatorcontrib>ZHANG LU</creatorcontrib><creatorcontrib>WANG ZONGYAO</creatorcontrib><creatorcontrib>ZHAO ZEYU</creatorcontrib><creatorcontrib>WANG YONGJIE</creatorcontrib><creatorcontrib>DU JUN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LUO LANG</au><au>PAN XIAOLU</au><au>LYU JIAWEI</au><au>TANG WEI</au><au>DING GUILI</au><au>ZHANG ZIXI</au><au>KANG BING</au><au>TONG XIN</au><au>XU ZHIHAO</au><au>WU DIWEI</au><au>HE JIAHUI</au><au>DENG HUAPU</au><au>HOU CHENG</au><au>WU XIAORUI</au><au>LI JIA</au><au>YANG FENGFAN</au><au>GAO MUFENG</au><au>HE QI</au><au>ZHANG LU</au><au>WANG ZONGYAO</au><au>ZHAO ZEYU</au><au>WANG YONGJIE</au><au>DU JUN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Oil-immersed transformer fault diagnosis method based on multi-source data fusion</title><date>2024-03-29</date><risdate>2024</risdate><abstract>The invention belongs to the technical field of transformer fault diagnosis, and relates to an oil-immersed transformer fault diagnosis method based on multi-source data fusion, which comprises the following steps of: selecting five gases as indexes for analyzing gas dissolved in transformer oil, a winding direct-current resistance unbalance coefficient, a winding absorption ratio, a polarization index, an iron core grounding current and a micro-water content; constructing a multi-source information fusion index; the multi-source information fusion indexes and the corresponding transformer fault types form a data set, and the data set is input into an SVM network for training; kernel function parameters and penalty parameters are optimized by adopting a zebra algorithm, and the established ZOA-SVM transformer fault diagnosis model is used for transformer fault diagnosis. According to the method, various state information amounts are analyzed through multi-source information fusion of the transformer, the SVM</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN117786516A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Oil-immersed transformer fault diagnosis method based on multi-source data fusion |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T10%3A20%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LUO%20LANG&rft.date=2024-03-29&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117786516A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |