A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification
With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Te...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2024, Vol.12, p.75249-75261 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 75261 |
|---|---|
| container_issue | |
| container_start_page | 75249 |
| container_title | IEEE access |
| container_volume | 12 |
| creator | Cai, Jiajun Wang, Huaizhi Jiang, Hui |
| description | With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs. |
| doi_str_mv | 10.1109/ACCESS.2024.3406164 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3062737675</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10539990</ieee_id><doaj_id>oai_doaj_org_article_1eb6ba9cf7184c599a849e1aebc78b49</doaj_id><sourcerecordid>3062737675</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-4263077b402a3834ad046bd73101152d1379cef39db38a440f3a2a099829cf943</originalsourceid><addsrcrecordid>eNpNkdtu1DAQhiMEElXbJ4ALS1xn8Sl2fLmEpVSqOGjLtTV2JsVLNg520qpvT7apUOdmDprvH2n-onjH6IYxaj5um2a332845XIjJFVMyVfFGWfKlKIS6vWL-m1xmfOBLlEvo0qfFfOW3OJxjAl6shsyHl0fhjvyCTK2ZI_HUO7nEdN9OPVXCcbfpInDfeznKcRhgb7h9BDTH9LFRH7EB0zk5wx9mB7J55CnOTkYPGbS9JBz6IKHE3dRvOmgz3j5nM-LX192t83X8ub71XWzvSk9r81USq4E1dpJykHUQkJLpXKtFowyVvGWCW08dsK0TtQgJe0EcKDG1Nz4zkhxXlyvum2Egx1TOEJ6tBGCfRrEdGchTcH3aBk65WDBNKulr4yBWhpkgM7r2kmzaH1YtcYU_86YJ3uIc1pekK2gimuhla6WLbFu-RRzTtj9v8qoPdllV7vsyS77bNdCvV-pgIgviEoYY6j4BwDakYE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3062737675</pqid></control><display><type>article</type><title>A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Cai, Jiajun ; Wang, Huaizhi ; Jiang, Hui</creator><creatorcontrib>Cai, Jiajun ; Wang, Huaizhi ; Jiang, Hui</creatorcontrib><description>With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2024.3406164</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Artificial neural networks ; Classification ; Classification algorithms ; Convolutional neural networks ; Disturbances ; Feature extraction ; graph convolutional network ; Graph neural networks ; Graph theory ; Graphical models ; Human error ; Labeling ; Labels ; Machine learning ; Noise tolerance ; Power grids ; Power quality ; Semisupervised learning ; Supervised learning ; temporal ensembling ; Time series analysis ; visibility graph</subject><ispartof>IEEE access, 2024, Vol.12, p.75249-75261</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c289t-4263077b402a3834ad046bd73101152d1379cef39db38a440f3a2a099829cf943</cites><orcidid>0009-0005-3711-4436 ; 0000-0001-5458-9154 ; 0000-0002-6257-8145</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10539990$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Cai, Jiajun</creatorcontrib><creatorcontrib>Wang, Huaizhi</creatorcontrib><creatorcontrib>Jiang, Hui</creatorcontrib><title>A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification</title><title>IEEE access</title><addtitle>Access</addtitle><description>With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Classification algorithms</subject><subject>Convolutional neural networks</subject><subject>Disturbances</subject><subject>Feature extraction</subject><subject>graph convolutional network</subject><subject>Graph neural networks</subject><subject>Graph theory</subject><subject>Graphical models</subject><subject>Human error</subject><subject>Labeling</subject><subject>Labels</subject><subject>Machine learning</subject><subject>Noise tolerance</subject><subject>Power grids</subject><subject>Power quality</subject><subject>Semisupervised learning</subject><subject>Supervised learning</subject><subject>temporal ensembling</subject><subject>Time series analysis</subject><subject>visibility graph</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkdtu1DAQhiMEElXbJ4ALS1xn8Sl2fLmEpVSqOGjLtTV2JsVLNg520qpvT7apUOdmDprvH2n-onjH6IYxaj5um2a332845XIjJFVMyVfFGWfKlKIS6vWL-m1xmfOBLlEvo0qfFfOW3OJxjAl6shsyHl0fhjvyCTK2ZI_HUO7nEdN9OPVXCcbfpInDfeznKcRhgb7h9BDTH9LFRH7EB0zk5wx9mB7J55CnOTkYPGbS9JBz6IKHE3dRvOmgz3j5nM-LX192t83X8ub71XWzvSk9r81USq4E1dpJykHUQkJLpXKtFowyVvGWCW08dsK0TtQgJe0EcKDG1Nz4zkhxXlyvum2Egx1TOEJ6tBGCfRrEdGchTcH3aBk65WDBNKulr4yBWhpkgM7r2kmzaH1YtcYU_86YJ3uIc1pekK2gimuhla6WLbFu-RRzTtj9v8qoPdllV7vsyS77bNdCvV-pgIgviEoYY6j4BwDakYE</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Cai, Jiajun</creator><creator>Wang, Huaizhi</creator><creator>Jiang, Hui</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0005-3711-4436</orcidid><orcidid>https://orcid.org/0000-0001-5458-9154</orcidid><orcidid>https://orcid.org/0000-0002-6257-8145</orcidid></search><sort><creationdate>2024</creationdate><title>A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification</title><author>Cai, Jiajun ; Wang, Huaizhi ; Jiang, Hui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-4263077b402a3834ad046bd73101152d1379cef39db38a440f3a2a099829cf943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Classification</topic><topic>Classification algorithms</topic><topic>Convolutional neural networks</topic><topic>Disturbances</topic><topic>Feature extraction</topic><topic>graph convolutional network</topic><topic>Graph neural networks</topic><topic>Graph theory</topic><topic>Graphical models</topic><topic>Human error</topic><topic>Labeling</topic><topic>Labels</topic><topic>Machine learning</topic><topic>Noise tolerance</topic><topic>Power grids</topic><topic>Power quality</topic><topic>Semisupervised learning</topic><topic>Supervised learning</topic><topic>temporal ensembling</topic><topic>Time series analysis</topic><topic>visibility graph</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cai, Jiajun</creatorcontrib><creatorcontrib>Wang, Huaizhi</creatorcontrib><creatorcontrib>Jiang, Hui</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cai, Jiajun</au><au>Wang, Huaizhi</au><au>Jiang, Hui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2024</date><risdate>2024</risdate><volume>12</volume><spage>75249</spage><epage>75261</epage><pages>75249-75261</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2024.3406164</doi><tpages>13</tpages><orcidid>https://orcid.org/0009-0005-3711-4436</orcidid><orcidid>https://orcid.org/0000-0001-5458-9154</orcidid><orcidid>https://orcid.org/0000-0002-6257-8145</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2024, Vol.12, p.75249-75261 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_proquest_journals_3062737675 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Artificial neural networks Classification Classification algorithms Convolutional neural networks Disturbances Feature extraction graph convolutional network Graph neural networks Graph theory Graphical models Human error Labeling Labels Machine learning Noise tolerance Power grids Power quality Semisupervised learning Supervised learning temporal ensembling Time series analysis visibility graph |
| title | A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T12%3A21%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Temporal%20Ensembling%20Based%20Semi-Supervised%20Graph%20Convolutional%20Network%20for%20Power%20Quality%20Disturbances%20Classification&rft.jtitle=IEEE%20access&rft.au=Cai,%20Jiajun&rft.date=2024&rft.volume=12&rft.spage=75249&rft.epage=75261&rft.pages=75249-75261&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2024.3406164&rft_dat=%3Cproquest_cross%3E3062737675%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3062737675&rft_id=info:pmid/&rft_ieee_id=10539990&rft_doaj_id=oai_doaj_org_article_1eb6ba9cf7184c599a849e1aebc78b49&rfr_iscdi=true |