MACHINE LEARNING FOR POWER GRID
A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | 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 | IEROME STEVE RUDIN CYNTHIA DUTTA HAIMONTI GROSS PHIL WALTZ DAVID BERT HUANG PASSONNEAU REBECCA J ANDERSON ROGER N KRESSNER ARTHUR HOFMANN PETER RADEVA AXINIA SALLEB-AOUISSI ANSAF BOULANGER ALBERT WU LEON L DOUGHERTY FRANK CHOW MAGGIE ISAAC DELFINA |
| description | A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2013232094A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2013232094A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2013232094A13</originalsourceid><addsrcrecordid>eNrjZJD3dXT28PRzVfBxdQzy8_RzV3DzD1II8A93DVJwD_J04WFgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFicmpeakl8aHBRgaGxkbGRgaWJo6GxsSpAgBCmiJN</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MACHINE LEARNING FOR POWER GRID</title><source>esp@cenet</source><creator>IEROME STEVE ; RUDIN CYNTHIA ; DUTTA HAIMONTI ; GROSS PHIL ; WALTZ DAVID ; BERT HUANG ; PASSONNEAU REBECCA J ; ANDERSON ROGER N ; KRESSNER ARTHUR ; HOFMANN PETER ; RADEVA AXINIA ; SALLEB-AOUISSI ANSAF ; BOULANGER ALBERT ; WU LEON L ; DOUGHERTY FRANK ; CHOW MAGGIE ; ISAAC DELFINA</creator><creatorcontrib>IEROME STEVE ; RUDIN CYNTHIA ; DUTTA HAIMONTI ; GROSS PHIL ; WALTZ DAVID ; BERT HUANG ; PASSONNEAU REBECCA J ; ANDERSON ROGER N ; KRESSNER ARTHUR ; HOFMANN PETER ; RADEVA AXINIA ; SALLEB-AOUISSI ANSAF ; BOULANGER ALBERT ; WU LEON L ; DOUGHERTY FRANK ; CHOW MAGGIE ; ISAAC DELFINA</creatorcontrib><description>A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2013</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=20130905&DB=EPODOC&CC=US&NR=2013232094A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20130905&DB=EPODOC&CC=US&NR=2013232094A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>IEROME STEVE</creatorcontrib><creatorcontrib>RUDIN CYNTHIA</creatorcontrib><creatorcontrib>DUTTA HAIMONTI</creatorcontrib><creatorcontrib>GROSS PHIL</creatorcontrib><creatorcontrib>WALTZ DAVID</creatorcontrib><creatorcontrib>BERT HUANG</creatorcontrib><creatorcontrib>PASSONNEAU REBECCA J</creatorcontrib><creatorcontrib>ANDERSON ROGER N</creatorcontrib><creatorcontrib>KRESSNER ARTHUR</creatorcontrib><creatorcontrib>HOFMANN PETER</creatorcontrib><creatorcontrib>RADEVA AXINIA</creatorcontrib><creatorcontrib>SALLEB-AOUISSI ANSAF</creatorcontrib><creatorcontrib>BOULANGER ALBERT</creatorcontrib><creatorcontrib>WU LEON L</creatorcontrib><creatorcontrib>DOUGHERTY FRANK</creatorcontrib><creatorcontrib>CHOW MAGGIE</creatorcontrib><creatorcontrib>ISAAC DELFINA</creatorcontrib><title>MACHINE LEARNING FOR POWER GRID</title><description>A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2013</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZJD3dXT28PRzVfBxdQzy8_RzV3DzD1II8A93DVJwD_J04WFgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFicmpeakl8aHBRgaGxkbGRgaWJo6GxsSpAgBCmiJN</recordid><startdate>20130905</startdate><enddate>20130905</enddate><creator>IEROME STEVE</creator><creator>RUDIN CYNTHIA</creator><creator>DUTTA HAIMONTI</creator><creator>GROSS PHIL</creator><creator>WALTZ DAVID</creator><creator>BERT HUANG</creator><creator>PASSONNEAU REBECCA J</creator><creator>ANDERSON ROGER N</creator><creator>KRESSNER ARTHUR</creator><creator>HOFMANN PETER</creator><creator>RADEVA AXINIA</creator><creator>SALLEB-AOUISSI ANSAF</creator><creator>BOULANGER ALBERT</creator><creator>WU LEON L</creator><creator>DOUGHERTY FRANK</creator><creator>CHOW MAGGIE</creator><creator>ISAAC DELFINA</creator><scope>EVB</scope></search><sort><creationdate>20130905</creationdate><title>MACHINE LEARNING FOR POWER GRID</title><author>IEROME STEVE ; RUDIN CYNTHIA ; DUTTA HAIMONTI ; GROSS PHIL ; WALTZ DAVID ; BERT HUANG ; PASSONNEAU REBECCA J ; ANDERSON ROGER N ; KRESSNER ARTHUR ; HOFMANN PETER ; RADEVA AXINIA ; SALLEB-AOUISSI ANSAF ; BOULANGER ALBERT ; WU LEON L ; DOUGHERTY FRANK ; CHOW MAGGIE ; ISAAC DELFINA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2013232094A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2013</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>IEROME STEVE</creatorcontrib><creatorcontrib>RUDIN CYNTHIA</creatorcontrib><creatorcontrib>DUTTA HAIMONTI</creatorcontrib><creatorcontrib>GROSS PHIL</creatorcontrib><creatorcontrib>WALTZ DAVID</creatorcontrib><creatorcontrib>BERT HUANG</creatorcontrib><creatorcontrib>PASSONNEAU REBECCA J</creatorcontrib><creatorcontrib>ANDERSON ROGER N</creatorcontrib><creatorcontrib>KRESSNER ARTHUR</creatorcontrib><creatorcontrib>HOFMANN PETER</creatorcontrib><creatorcontrib>RADEVA AXINIA</creatorcontrib><creatorcontrib>SALLEB-AOUISSI ANSAF</creatorcontrib><creatorcontrib>BOULANGER ALBERT</creatorcontrib><creatorcontrib>WU LEON L</creatorcontrib><creatorcontrib>DOUGHERTY FRANK</creatorcontrib><creatorcontrib>CHOW MAGGIE</creatorcontrib><creatorcontrib>ISAAC DELFINA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>IEROME STEVE</au><au>RUDIN CYNTHIA</au><au>DUTTA HAIMONTI</au><au>GROSS PHIL</au><au>WALTZ DAVID</au><au>BERT HUANG</au><au>PASSONNEAU REBECCA J</au><au>ANDERSON ROGER N</au><au>KRESSNER ARTHUR</au><au>HOFMANN PETER</au><au>RADEVA AXINIA</au><au>SALLEB-AOUISSI ANSAF</au><au>BOULANGER ALBERT</au><au>WU LEON L</au><au>DOUGHERTY FRANK</au><au>CHOW MAGGIE</au><au>ISAAC DELFINA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MACHINE LEARNING FOR POWER GRID</title><date>2013-09-05</date><risdate>2013</risdate><abstract>A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US2013232094A1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | MACHINE LEARNING FOR POWER GRID |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T22%3A14%3A34IST&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=IEROME%20STEVE&rft.date=2013-09-05&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2013232094A1%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 |