Partial Discharge Monitoring System And Partial Discharge Monitoring Method
The present invention relates to a partial discharge monitoring system and a partial discharge monitoring method, which is configured to: monitor and determine a defect occurring in a high-voltage power device in real time by classifying and pattern-recognizing a signal generated from the high-volta...
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| creator | JANG KYUNG HOON LEE HYEON SEOK PARK DAE JIN SHIM SUNG IK SAKAMOTO KUNIAKI KIM SUNG YUN |
| description | The present invention relates to a partial discharge monitoring system and a partial discharge monitoring method, which is configured to: monitor and determine a defect occurring in a high-voltage power device in real time by classifying and pattern-recognizing a signal generated from the high-voltage power device by applying a machine learning algorithm; and easily form a feature point data cluster in both a high-density area and a low-density area of two-dimensional feature point data by performing a process of clustering feature points generated from signals of the power device on the basis of the density and the distance in parallel. Therefore, the present invention can generate PRPD data for each cluster without missing a signal and thus can improve partial discharge determination accuracy.
본 발명은 기계학습 알고리즘을 적용하여 고전압 전력기기에서 발생하는 신호를 분류 및 패턴 인식하여 고전압 전력기기에 발생된 결함을 실시간으로 모니터링 및 판정할 수 있고, 전력기기의 신호로부터 생성된 특징점 포인트를 각각 밀도와 거리 기반으로 군집하는 과정을 병행하여, 이차원 특징점 데이터의 고밀도 영역 및 저밀도 영역 모두에서 특징점 데이터 군집을 용이하게 형성 가능하므로, 신호의 누락없이 군집별 PRPD 데이터를 생성하여 부분방전 판정 정확도를 향상시킬 수 있는 부분방전 모니터링 시스템 및 부분방전 모니터링 방법에 관한 것이다. |
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본 발명은 기계학습 알고리즘을 적용하여 고전압 전력기기에서 발생하는 신호를 분류 및 패턴 인식하여 고전압 전력기기에 발생된 결함을 실시간으로 모니터링 및 판정할 수 있고, 전력기기의 신호로부터 생성된 특징점 포인트를 각각 밀도와 거리 기반으로 군집하는 과정을 병행하여, 이차원 특징점 데이터의 고밀도 영역 및 저밀도 영역 모두에서 특징점 데이터 군집을 용이하게 형성 가능하므로, 신호의 누락없이 군집별 PRPD 데이터를 생성하여 부분방전 판정 정확도를 향상시킬 수 있는 부분방전 모니터링 시스템 및 부분방전 모니터링 방법에 관한 것이다.</description><language>eng ; kor</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; MEASURING ; MEASURING ELECTRIC VARIABLES ; MEASURING MAGNETIC VARIABLES ; PHYSICS ; TESTING</subject><creationdate>2023</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=20230710&DB=EPODOC&CC=KR&NR=20230104527A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230710&DB=EPODOC&CC=KR&NR=20230104527A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>JANG KYUNG HOON</creatorcontrib><creatorcontrib>LEE HYEON SEOK</creatorcontrib><creatorcontrib>PARK DAE JIN</creatorcontrib><creatorcontrib>SHIM SUNG IK</creatorcontrib><creatorcontrib>SAKAMOTO KUNIAKI</creatorcontrib><creatorcontrib>KIM SUNG YUN</creatorcontrib><title>Partial Discharge Monitoring System And Partial Discharge Monitoring Method</title><description>The present invention relates to a partial discharge monitoring system and a partial discharge monitoring method, which is configured to: monitor and determine a defect occurring in a high-voltage power device in real time by classifying and pattern-recognizing a signal generated from the high-voltage power device by applying a machine learning algorithm; and easily form a feature point data cluster in both a high-density area and a low-density area of two-dimensional feature point data by performing a process of clustering feature points generated from signals of the power device on the basis of the density and the distance in parallel. Therefore, the present invention can generate PRPD data for each cluster without missing a signal and thus can improve partial discharge determination accuracy.
본 발명은 기계학습 알고리즘을 적용하여 고전압 전력기기에서 발생하는 신호를 분류 및 패턴 인식하여 고전압 전력기기에 발생된 결함을 실시간으로 모니터링 및 판정할 수 있고, 전력기기의 신호로부터 생성된 특징점 포인트를 각각 밀도와 거리 기반으로 군집하는 과정을 병행하여, 이차원 특징점 데이터의 고밀도 영역 및 저밀도 영역 모두에서 특징점 데이터 군집을 용이하게 형성 가능하므로, 신호의 누락없이 군집별 PRPD 데이터를 생성하여 부분방전 판정 정확도를 향상시킬 수 있는 부분방전 모니터링 시스템 및 부분방전 모니터링 방법에 관한 것이다.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPAOSCwqyUzMUXDJLE7OSCxKT1Xwzc_LLMkvysxLVwiuLC5JzVVwzEtRwKvON7UkIz-Fh4E1LTGnOJUXSnMzKLu5hjh76KYW5MenFhckJqfmpZbEewcZGRgZGxgamJgamTsaE6cKAAohNfU</recordid><startdate>20230710</startdate><enddate>20230710</enddate><creator>JANG KYUNG HOON</creator><creator>LEE HYEON SEOK</creator><creator>PARK DAE JIN</creator><creator>SHIM SUNG IK</creator><creator>SAKAMOTO KUNIAKI</creator><creator>KIM SUNG YUN</creator><scope>EVB</scope></search><sort><creationdate>20230710</creationdate><title>Partial Discharge Monitoring System And Partial Discharge Monitoring Method</title><author>JANG KYUNG HOON ; LEE HYEON SEOK ; PARK DAE JIN ; SHIM SUNG IK ; SAKAMOTO KUNIAKI ; KIM SUNG YUN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_KR20230104527A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; kor</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</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>JANG KYUNG HOON</creatorcontrib><creatorcontrib>LEE HYEON SEOK</creatorcontrib><creatorcontrib>PARK DAE JIN</creatorcontrib><creatorcontrib>SHIM SUNG IK</creatorcontrib><creatorcontrib>SAKAMOTO KUNIAKI</creatorcontrib><creatorcontrib>KIM SUNG YUN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>JANG KYUNG HOON</au><au>LEE HYEON SEOK</au><au>PARK DAE JIN</au><au>SHIM SUNG IK</au><au>SAKAMOTO KUNIAKI</au><au>KIM SUNG YUN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Partial Discharge Monitoring System And Partial Discharge Monitoring Method</title><date>2023-07-10</date><risdate>2023</risdate><abstract>The present invention relates to a partial discharge monitoring system and a partial discharge monitoring method, which is configured to: monitor and determine a defect occurring in a high-voltage power device in real time by classifying and pattern-recognizing a signal generated from the high-voltage power device by applying a machine learning algorithm; and easily form a feature point data cluster in both a high-density area and a low-density area of two-dimensional feature point data by performing a process of clustering feature points generated from signals of the power device on the basis of the density and the distance in parallel. Therefore, the present invention can generate PRPD data for each cluster without missing a signal and thus can improve partial discharge determination accuracy.
본 발명은 기계학습 알고리즘을 적용하여 고전압 전력기기에서 발생하는 신호를 분류 및 패턴 인식하여 고전압 전력기기에 발생된 결함을 실시간으로 모니터링 및 판정할 수 있고, 전력기기의 신호로부터 생성된 특징점 포인트를 각각 밀도와 거리 기반으로 군집하는 과정을 병행하여, 이차원 특징점 데이터의 고밀도 영역 및 저밀도 영역 모두에서 특징점 데이터 군집을 용이하게 형성 가능하므로, 신호의 누락없이 군집별 PRPD 데이터를 생성하여 부분방전 판정 정확도를 향상시킬 수 있는 부분방전 모니터링 시스템 및 부분방전 모니터링 방법에 관한 것이다.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING MEASURING MEASURING ELECTRIC VARIABLES MEASURING MAGNETIC VARIABLES PHYSICS TESTING |
| title | Partial Discharge Monitoring System And Partial Discharge Monitoring Method |
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