A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform
Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (AS...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2018-10, Vol.67 (10), p.2262-2272 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2272 |
|---|---|
| container_issue | 10 |
| container_start_page | 2262 |
| container_title | IEEE transactions on instrumentation and measurement |
| container_volume | 67 |
| creator | Ghorat, Mohsen Gharehpetian, G. B. Latifi, Hamid Hejazi, Maryam A. |
| description | Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization. |
| doi_str_mv | 10.1109/TIM.2018.2816438 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2110102746</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8331927</ieee_id><sourcerecordid>2110102746</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-84541615895581c5931364c52f9e0d05d619dd327411cceeebf3ea839f63a7a63</originalsourceid><addsrcrecordid>eNo9kMtLw0AQhxdRsFbvgpcFz6k72ewme4ytj0J9gBGPYU0m6Za83E2r_vcmtHgamPl9M8xHyCWwGQBTN8nyaeYziGZ-BDLg0RGZgBChp6T0j8mEDSNPBUKekjPnNoyxUAbhhKxj-ozf9FXb3uiKLozL1tqWSN9M2YwNbFrjTFPSuCpba_p1TW-1w5y2DY1z3fVmh3Sx1ZWXWEQ6b-uuwh_6oXdYYU8TqxtXtLY-JyeFrhxeHOqUvN_fJfNHb_XysJzHKy_zFfReFIgAJIhICRFBJhQHLoNM-IVCljORS1B5zv0wAMgyRPwsOOqIq0JyHWrJp-R6v7ez7dcWXZ9u2q0dXnGpP4gCNqBjiu1TmW2ds1iknTW1tr8psHT0mQ4-09FnevA5IFd7xAxX_-MR56D8kP8BtOFwDw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2110102746</pqid></control><display><type>article</type><title>A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform</title><source>IEEE Electronic Library (IEL)</source><creator>Ghorat, Mohsen ; Gharehpetian, G. B. ; Latifi, Hamid ; Hejazi, Maryam A.</creator><creatorcontrib>Ghorat, Mohsen ; Gharehpetian, G. B. ; Latifi, Hamid ; Hejazi, Maryam A.</creatorcontrib><description>Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2018.2816438</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive algorithms ; Algorithms ; Computer simulation ; Denoising ; Discharge ; Discrete wavelet transforms ; Eigenvalues and eigenfunctions ; Image reconstruction ; Noise reduction ; partial discharge (PD) ; Partial discharges ; Singular value decomposition ; singular value decomposition (SVD) ; wavelet transform ; Wavelet transforms</subject><ispartof>IEEE transactions on instrumentation and measurement, 2018-10, Vol.67 (10), p.2262-2272</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-84541615895581c5931364c52f9e0d05d619dd327411cceeebf3ea839f63a7a63</citedby><cites>FETCH-LOGICAL-c291t-84541615895581c5931364c52f9e0d05d619dd327411cceeebf3ea839f63a7a63</cites><orcidid>0000-0003-1521-790X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8331927$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8331927$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ghorat, Mohsen</creatorcontrib><creatorcontrib>Gharehpetian, G. B.</creatorcontrib><creatorcontrib>Latifi, Hamid</creatorcontrib><creatorcontrib>Hejazi, Maryam A.</creatorcontrib><title>A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Computer simulation</subject><subject>Denoising</subject><subject>Discharge</subject><subject>Discrete wavelet transforms</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Image reconstruction</subject><subject>Noise reduction</subject><subject>partial discharge (PD)</subject><subject>Partial discharges</subject><subject>Singular value decomposition</subject><subject>singular value decomposition (SVD)</subject><subject>wavelet transform</subject><subject>Wavelet transforms</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtLw0AQhxdRsFbvgpcFz6k72ewme4ytj0J9gBGPYU0m6Za83E2r_vcmtHgamPl9M8xHyCWwGQBTN8nyaeYziGZ-BDLg0RGZgBChp6T0j8mEDSNPBUKekjPnNoyxUAbhhKxj-ozf9FXb3uiKLozL1tqWSN9M2YwNbFrjTFPSuCpba_p1TW-1w5y2DY1z3fVmh3Sx1ZWXWEQ6b-uuwh_6oXdYYU8TqxtXtLY-JyeFrhxeHOqUvN_fJfNHb_XysJzHKy_zFfReFIgAJIhICRFBJhQHLoNM-IVCljORS1B5zv0wAMgyRPwsOOqIq0JyHWrJp-R6v7ez7dcWXZ9u2q0dXnGpP4gCNqBjiu1TmW2ds1iknTW1tr8psHT0mQ4-09FnevA5IFd7xAxX_-MR56D8kP8BtOFwDw</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Ghorat, Mohsen</creator><creator>Gharehpetian, G. B.</creator><creator>Latifi, Hamid</creator><creator>Hejazi, Maryam A.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-1521-790X</orcidid></search><sort><creationdate>20181001</creationdate><title>A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform</title><author>Ghorat, Mohsen ; Gharehpetian, G. B. ; Latifi, Hamid ; Hejazi, Maryam A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-84541615895581c5931364c52f9e0d05d619dd327411cceeebf3ea839f63a7a63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Computer simulation</topic><topic>Denoising</topic><topic>Discharge</topic><topic>Discrete wavelet transforms</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Image reconstruction</topic><topic>Noise reduction</topic><topic>partial discharge (PD)</topic><topic>Partial discharges</topic><topic>Singular value decomposition</topic><topic>singular value decomposition (SVD)</topic><topic>wavelet transform</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghorat, Mohsen</creatorcontrib><creatorcontrib>Gharehpetian, G. B.</creatorcontrib><creatorcontrib>Latifi, Hamid</creatorcontrib><creatorcontrib>Hejazi, Maryam A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ghorat, Mohsen</au><au>Gharehpetian, G. B.</au><au>Latifi, Hamid</au><au>Hejazi, Maryam A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2018-10-01</date><risdate>2018</risdate><volume>67</volume><issue>10</issue><spage>2262</spage><epage>2272</epage><pages>2262-2272</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIM.2018.2816438</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-1521-790X</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9456 |
| ispartof | IEEE transactions on instrumentation and measurement, 2018-10, Vol.67 (10), p.2262-2272 |
| issn | 0018-9456 1557-9662 |
| language | eng |
| recordid | cdi_proquest_journals_2110102746 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive algorithms Algorithms Computer simulation Denoising Discharge Discrete wavelet transforms Eigenvalues and eigenfunctions Image reconstruction Noise reduction partial discharge (PD) Partial discharges Singular value decomposition singular value decomposition (SVD) wavelet transform Wavelet transforms |
| title | A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T02%3A32%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20New%20Partial%20Discharge%20Signal%20Denoising%20Algorithm%20Based%20on%20Adaptive%20Dual-Tree%20Complex%20Wavelet%20Transform&rft.jtitle=IEEE%20transactions%20on%20instrumentation%20and%20measurement&rft.au=Ghorat,%20Mohsen&rft.date=2018-10-01&rft.volume=67&rft.issue=10&rft.spage=2262&rft.epage=2272&rft.pages=2262-2272&rft.issn=0018-9456&rft.eissn=1557-9662&rft.coden=IEIMAO&rft_id=info:doi/10.1109/TIM.2018.2816438&rft_dat=%3Cproquest_RIE%3E2110102746%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2110102746&rft_id=info:pmid/&rft_ieee_id=8331927&rfr_iscdi=true |