Data enhancement partial discharge detection method based on generalized S transformation

The invention relates to a data enhancement partial discharge detection method based on generalized S transformation, and belongs to the field of partial discharge detection. The method comprises the following steps: carrying out generalized S transformation of an initialization parameter on a sampl...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	WU HAIYAN, HOU SHUHAN, HUANG XINGYU, ZHANG YI, ZHOU PENG, JIANG JIANGSONG, LIAO JINGSONG, YANG CHUAN
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING ELECTRIC VARIABLES MEASURING MAGNETIC VARIABLES PHYSICS TESTING
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	WU HAIYAN HOU SHUHAN HUANG XINGYU ZHANG YI ZHOU PENG JIANG JIANGSONG LIAO JINGSONG YANG CHUAN
description	The invention relates to a data enhancement partial discharge detection method based on generalized S transformation, and belongs to the field of partial discharge detection. The method comprises the following steps: carrying out generalized S transformation of an initialization parameter on a sampling signal to obtain a time-frequency distribution grey-scale map of a partial discharge signal; inputting the obtained time-frequency distribution grey-scale map into a lightweight neural network, and calculating the average loss value and the accuracy rate of the model after each round of training; constructing a multi-target nonlinear optimization problem with the minimum average loss value and the maximum average accuracy; the optimal solution for solving the optimization problem is used for updating parameters of generalized S transformation and used for model training of the next round; deploying the trained lightweight model to micro MUC hardware for online identification of partial discharge of electrical e
format	Patent
fullrecord	<record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117851864A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117851864A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117851864A3</originalsourceid><addsrcrecordid>eNqNizsOwjAQBdNQIOAOywEoIn5powCiooGGKlrsl9iSs47srTg9QeIAVKORZubF88TKBHEsBgNEaeSkngNZn43j1IMsFEZ9FBqgLlp6cYalyXsIEgf_nvROmlhyF9PA33hZzDoOGasfF8X6cn401w3G2CKPbKZZ2-ZWlsdqX1aHXb39p_kACU47GQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Data enhancement partial discharge detection method based on generalized S transformation</title><source>esp@cenet</source><creator>WU HAIYAN ; HOU SHUHAN ; HUANG XINGYU ; ZHANG YI ; ZHOU PENG ; JIANG JIANGSONG ; LIAO JINGSONG ; YANG CHUAN</creator><creatorcontrib>WU HAIYAN ; HOU SHUHAN ; HUANG XINGYU ; ZHANG YI ; ZHOU PENG ; JIANG JIANGSONG ; LIAO JINGSONG ; YANG CHUAN</creatorcontrib><description>The invention relates to a data enhancement partial discharge detection method based on generalized S transformation, and belongs to the field of partial discharge detection. The method comprises the following steps: carrying out generalized S transformation of an initialization parameter on a sampling signal to obtain a time-frequency distribution grey-scale map of a partial discharge signal; inputting the obtained time-frequency distribution grey-scale map into a lightweight neural network, and calculating the average loss value and the accuracy rate of the model after each round of training; constructing a multi-target nonlinear optimization problem with the minimum average loss value and the maximum average accuracy; the optimal solution for solving the optimization problem is used for updating parameters of generalized S transformation and used for model training of the next round; deploying the trained lightweight model to micro MUC hardware for online identification of partial discharge of electrical e</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=20240409&DB=EPODOC&CC=CN&NR=117851864A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76418</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240409&DB=EPODOC&CC=CN&NR=117851864A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WU HAIYAN</creatorcontrib><creatorcontrib>HOU SHUHAN</creatorcontrib><creatorcontrib>HUANG XINGYU</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>ZHOU PENG</creatorcontrib><creatorcontrib>JIANG JIANGSONG</creatorcontrib><creatorcontrib>LIAO JINGSONG</creatorcontrib><creatorcontrib>YANG CHUAN</creatorcontrib><title>Data enhancement partial discharge detection method based on generalized S transformation</title><description>The invention relates to a data enhancement partial discharge detection method based on generalized S transformation, and belongs to the field of partial discharge detection. The method comprises the following steps: carrying out generalized S transformation of an initialization parameter on a sampling signal to obtain a time-frequency distribution grey-scale map of a partial discharge signal; inputting the obtained time-frequency distribution grey-scale map into a lightweight neural network, and calculating the average loss value and the accuracy rate of the model after each round of training; constructing a multi-target nonlinear optimization problem with the minimum average loss value and the maximum average accuracy; the optimal solution for solving the optimization problem is used for updating parameters of generalized S transformation and used for model training of the next round; deploying the trained lightweight model to micro MUC hardware for online identification of partial discharge of electrical e</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>MEASURING</subject><subject>MEASURING ELECTRIC VARIABLES</subject><subject>MEASURING MAGNETIC VARIABLES</subject><subject>PHYSICS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNizsOwjAQBdNQIOAOywEoIn5powCiooGGKlrsl9iSs47srTg9QeIAVKORZubF88TKBHEsBgNEaeSkngNZn43j1IMsFEZ9FBqgLlp6cYalyXsIEgf_nvROmlhyF9PA33hZzDoOGasfF8X6cn401w3G2CKPbKZZ2-ZWlsdqX1aHXb39p_kACU47GQ</recordid><startdate>20240409</startdate><enddate>20240409</enddate><creator>WU HAIYAN</creator><creator>HOU SHUHAN</creator><creator>HUANG XINGYU</creator><creator>ZHANG YI</creator><creator>ZHOU PENG</creator><creator>JIANG JIANGSONG</creator><creator>LIAO JINGSONG</creator><creator>YANG CHUAN</creator><scope>EVB</scope></search><sort><creationdate>20240409</creationdate><title>Data enhancement partial discharge detection method based on generalized S transformation</title><author>WU HAIYAN ; HOU SHUHAN ; HUANG XINGYU ; ZHANG YI ; ZHOU PENG ; JIANG JIANGSONG ; LIAO JINGSONG ; YANG CHUAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117851864A3</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>MEASURING</topic><topic>MEASURING ELECTRIC VARIABLES</topic><topic>MEASURING MAGNETIC VARIABLES</topic><topic>PHYSICS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>WU HAIYAN</creatorcontrib><creatorcontrib>HOU SHUHAN</creatorcontrib><creatorcontrib>HUANG XINGYU</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>ZHOU PENG</creatorcontrib><creatorcontrib>JIANG JIANGSONG</creatorcontrib><creatorcontrib>LIAO JINGSONG</creatorcontrib><creatorcontrib>YANG CHUAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WU HAIYAN</au><au>HOU SHUHAN</au><au>HUANG XINGYU</au><au>ZHANG YI</au><au>ZHOU PENG</au><au>JIANG JIANGSONG</au><au>LIAO JINGSONG</au><au>YANG CHUAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Data enhancement partial discharge detection method based on generalized S transformation</title><date>2024-04-09</date><risdate>2024</risdate><abstract>The invention relates to a data enhancement partial discharge detection method based on generalized S transformation, and belongs to the field of partial discharge detection. The method comprises the following steps: carrying out generalized S transformation of an initialization parameter on a sampling signal to obtain a time-frequency distribution grey-scale map of a partial discharge signal; inputting the obtained time-frequency distribution grey-scale map into a lightweight neural network, and calculating the average loss value and the accuracy rate of the model after each round of training; constructing a multi-target nonlinear optimization problem with the minimum average loss value and the maximum average accuracy; the optimal solution for solving the optimization problem is used for updating parameters of generalized S transformation and used for model training of the next round; deploying the trained lightweight model to micro MUC hardware for online identification of partial discharge of electrical e</abstract><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier
ispartof
issn
language	chi ; eng
recordid	cdi_epo_espacenet_CN117851864A
source	esp@cenet
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	Data enhancement partial discharge detection method based on generalized S transformation
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T03%3A15%3A18IST&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=WU%20HAIYAN&rft.date=2024-04-09&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117851864A%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