Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors
The invention discloses a power transmission line dynamic thermal capacity limit prediction method capable of tracking environmental factor time-varying characteristics, and belongs to the field of power transmission network line operation state evaluation in a power system. Firstly, main influence...
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 | JIN TIAN DOU YANAN HU SHUBO KO JUNG-NAM LU XUELI ZHU BAOHANG SUN HUI |
| description | The invention discloses a power transmission line dynamic thermal capacity limit prediction method capable of tracking environmental factor time-varying characteristics, and belongs to the field of power transmission network line operation state evaluation in a power system. Firstly, main influence factors of the DTR are determined; and establishing a DTR prediction model considering the influence degree time-varying characteristics of the environmental factors. Secondly, obtaining DTR time sequence prediction model training data, and training a DTR time sequence prediction model; and finally, performing DTR prediction by using the DTR time sequence prediction model, and finally realizing rolling prediction. According to the method, the FoecastNet prediction model of the time-varying weight structure is adopted, so that the problem that the environment input feature weight of a traditional time sequence prediction model is not changed can be effectively solved, the change of the influence degree of environmen |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115456301A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115456301A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115456301A3</originalsourceid><addsrcrecordid>eNqNjkEKwkAMRbtxIeod4gEKlqp7KYorceG-jNPUBmcyJRMqvYYndioewFX4eY_kz7P3NbxQQMVw9BQjBQZHjNCMbDxZ0A7FGwfW9MaSjol6UugFG7I66R61C81XuDuE0E7X7JP4AUoe88HIOAXbmbRXFIpKNk4i8kAS2CNretEmGiQus1lrXMTVby6y9el4q8459qHGmGogo9bVpSh2292-3BSH8h_nAy0pUY4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors</title><source>esp@cenet</source><creator>JIN TIAN ; DOU YANAN ; HU SHUBO ; KO JUNG-NAM ; LU XUELI ; ZHU BAOHANG ; SUN HUI</creator><creatorcontrib>JIN TIAN ; DOU YANAN ; HU SHUBO ; KO JUNG-NAM ; LU XUELI ; ZHU BAOHANG ; SUN HUI</creatorcontrib><description>The invention discloses a power transmission line dynamic thermal capacity limit prediction method capable of tracking environmental factor time-varying characteristics, and belongs to the field of power transmission network line operation state evaluation in a power system. Firstly, main influence factors of the DTR are determined; and establishing a DTR prediction model considering the influence degree time-varying characteristics of the environmental factors. Secondly, obtaining DTR time sequence prediction model training data, and training a DTR time sequence prediction model; and finally, performing DTR prediction by using the DTR time sequence prediction model, and finally realizing rolling prediction. According to the method, the FoecastNet prediction model of the time-varying weight structure is adopted, so that the problem that the environment input feature weight of a traditional time sequence prediction model is not changed can be effectively solved, the change of the influence degree of environmen</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2022</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=20221209&DB=EPODOC&CC=CN&NR=115456301A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221209&DB=EPODOC&CC=CN&NR=115456301A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>JIN TIAN</creatorcontrib><creatorcontrib>DOU YANAN</creatorcontrib><creatorcontrib>HU SHUBO</creatorcontrib><creatorcontrib>KO JUNG-NAM</creatorcontrib><creatorcontrib>LU XUELI</creatorcontrib><creatorcontrib>ZHU BAOHANG</creatorcontrib><creatorcontrib>SUN HUI</creatorcontrib><title>Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors</title><description>The invention discloses a power transmission line dynamic thermal capacity limit prediction method capable of tracking environmental factor time-varying characteristics, and belongs to the field of power transmission network line operation state evaluation in a power system. Firstly, main influence factors of the DTR are determined; and establishing a DTR prediction model considering the influence degree time-varying characteristics of the environmental factors. Secondly, obtaining DTR time sequence prediction model training data, and training a DTR time sequence prediction model; and finally, performing DTR prediction by using the DTR time sequence prediction model, and finally realizing rolling prediction. According to the method, the FoecastNet prediction model of the time-varying weight structure is adopted, so that the problem that the environment input feature weight of a traditional time sequence prediction model is not changed can be effectively solved, the change of the influence degree of environmen</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjkEKwkAMRbtxIeod4gEKlqp7KYorceG-jNPUBmcyJRMqvYYndioewFX4eY_kz7P3NbxQQMVw9BQjBQZHjNCMbDxZ0A7FGwfW9MaSjol6UugFG7I66R61C81XuDuE0E7X7JP4AUoe88HIOAXbmbRXFIpKNk4i8kAS2CNretEmGiQus1lrXMTVby6y9el4q8459qHGmGogo9bVpSh2292-3BSH8h_nAy0pUY4</recordid><startdate>20221209</startdate><enddate>20221209</enddate><creator>JIN TIAN</creator><creator>DOU YANAN</creator><creator>HU SHUBO</creator><creator>KO JUNG-NAM</creator><creator>LU XUELI</creator><creator>ZHU BAOHANG</creator><creator>SUN HUI</creator><scope>EVB</scope></search><sort><creationdate>20221209</creationdate><title>Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors</title><author>JIN TIAN ; DOU YANAN ; HU SHUBO ; KO JUNG-NAM ; LU XUELI ; ZHU BAOHANG ; SUN HUI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115456301A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>JIN TIAN</creatorcontrib><creatorcontrib>DOU YANAN</creatorcontrib><creatorcontrib>HU SHUBO</creatorcontrib><creatorcontrib>KO JUNG-NAM</creatorcontrib><creatorcontrib>LU XUELI</creatorcontrib><creatorcontrib>ZHU BAOHANG</creatorcontrib><creatorcontrib>SUN HUI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>JIN TIAN</au><au>DOU YANAN</au><au>HU SHUBO</au><au>KO JUNG-NAM</au><au>LU XUELI</au><au>ZHU BAOHANG</au><au>SUN HUI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors</title><date>2022-12-09</date><risdate>2022</risdate><abstract>The invention discloses a power transmission line dynamic thermal capacity limit prediction method capable of tracking environmental factor time-varying characteristics, and belongs to the field of power transmission network line operation state evaluation in a power system. Firstly, main influence factors of the DTR are determined; and establishing a DTR prediction model considering the influence degree time-varying characteristics of the environmental factors. Secondly, obtaining DTR time sequence prediction model training data, and training a DTR time sequence prediction model; and finally, performing DTR prediction by using the DTR time sequence prediction model, and finally realizing rolling prediction. According to the method, the FoecastNet prediction model of the time-varying weight structure is adopted, so that the problem that the environment input feature weight of a traditional time sequence prediction model is not changed can be effectively solved, the change of the influence degree of environmen</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN115456301A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Power transmission line dynamic thermal capacity limit prediction method capable of tracking time-varying characteristics of environmental factors |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T13%3A06%3A59IST&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=JIN%20TIAN&rft.date=2022-12-09&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115456301A%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 |