Loading…
Hybrid Deep Learning for Dynamic Total Transfer Capability Control
This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimizat...
Saved in:
| Published in: | IEEE transactions on power systems 2021-05, Vol.36 (3), p.2733-2736 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843 |
| container_end_page | 2736 |
| container_issue | 3 |
| container_start_page | 2733 |
| container_title | IEEE transactions on power systems |
| container_volume | 36 |
| creator | Gao, Qiu Liu, Youbo Zhao, Junbo Liu, Junyong Chung, Chi Yung |
| description | This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios. |
| doi_str_mv | 10.1109/TPWRS.2021.3057523 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2515850037</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9349169</ieee_id><sourcerecordid>2515850037</sourcerecordid><originalsourceid>FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843</originalsourceid><addsrcrecordid>eNo9kMFOwzAQRC0EEqXwA3CxxDll145j-wgpUKRKIAjiaDmJg1KlSbDTQ_6elFac9jDzZqVHyDXCAhH0Xfb29f6xYMBwwUFIwfgJmaEQKoJE6lMyA6VEpLSAc3IRwgYAkimYkYfVmPu6pEvnerp21rd1-02rztPl2NptXdCsG2xDM2_bUDlPU9vbvG7qYaRp1w6-ay7JWWWb4K6Od04-nx6zdBWtX59f0vt1VDAthggTWQiFPLellQKAJ8yCxRKV1Sq3qHIBcY4lqDIWsohjWWjNOJfcMUAV8zm5Pez2vvvZuTCYTbfz7fTSMIFC7Tfl1GKHVuG7ELyrTO_rrfWjQTB7V-bPldm7MkdXE3RzgGrn3D-geawx0fwXV7xjgg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2515850037</pqid></control><display><type>article</type><title>Hybrid Deep Learning for Dynamic Total Transfer Capability Control</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Gao, Qiu ; Liu, Youbo ; Zhao, Junbo ; Liu, Junyong ; Chung, Chi Yung</creator><creatorcontrib>Gao, Qiu ; Liu, Youbo ; Zhao, Junbo ; Liu, Junyong ; Chung, Chi Yung</creatorcontrib><description>This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2021.3057523</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>artificial intelligence ; Deep learning ; deep reinforcement learning ; distributed proximal policy optimization ; Generators ; Optimization ; Power system dynamics ; Security ; Stability criteria ; Total transfer capability ; Training ; Transient analysis</subject><ispartof>IEEE transactions on power systems, 2021-05, Vol.36 (3), p.2733-2736</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843</citedby><cites>FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843</cites><orcidid>0000-0001-5257-7280 ; 0000-0002-5465-5243 ; 0000-0001-6607-2240 ; 0000-0002-8498-9666</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9349169$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Gao, Qiu</creatorcontrib><creatorcontrib>Liu, Youbo</creatorcontrib><creatorcontrib>Zhao, Junbo</creatorcontrib><creatorcontrib>Liu, Junyong</creatorcontrib><creatorcontrib>Chung, Chi Yung</creatorcontrib><title>Hybrid Deep Learning for Dynamic Total Transfer Capability Control</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.</description><subject>artificial intelligence</subject><subject>Deep learning</subject><subject>deep reinforcement learning</subject><subject>distributed proximal policy optimization</subject><subject>Generators</subject><subject>Optimization</subject><subject>Power system dynamics</subject><subject>Security</subject><subject>Stability criteria</subject><subject>Total transfer capability</subject><subject>Training</subject><subject>Transient analysis</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNo9kMFOwzAQRC0EEqXwA3CxxDll145j-wgpUKRKIAjiaDmJg1KlSbDTQ_6elFac9jDzZqVHyDXCAhH0Xfb29f6xYMBwwUFIwfgJmaEQKoJE6lMyA6VEpLSAc3IRwgYAkimYkYfVmPu6pEvnerp21rd1-02rztPl2NptXdCsG2xDM2_bUDlPU9vbvG7qYaRp1w6-ay7JWWWb4K6Od04-nx6zdBWtX59f0vt1VDAthggTWQiFPLellQKAJ8yCxRKV1Sq3qHIBcY4lqDIWsohjWWjNOJfcMUAV8zm5Pez2vvvZuTCYTbfz7fTSMIFC7Tfl1GKHVuG7ELyrTO_rrfWjQTB7V-bPldm7MkdXE3RzgGrn3D-geawx0fwXV7xjgg</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Gao, Qiu</creator><creator>Liu, Youbo</creator><creator>Zhao, Junbo</creator><creator>Liu, Junyong</creator><creator>Chung, Chi Yung</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>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5257-7280</orcidid><orcidid>https://orcid.org/0000-0002-5465-5243</orcidid><orcidid>https://orcid.org/0000-0001-6607-2240</orcidid><orcidid>https://orcid.org/0000-0002-8498-9666</orcidid></search><sort><creationdate>202105</creationdate><title>Hybrid Deep Learning for Dynamic Total Transfer Capability Control</title><author>Gao, Qiu ; Liu, Youbo ; Zhao, Junbo ; Liu, Junyong ; Chung, Chi Yung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>artificial intelligence</topic><topic>Deep learning</topic><topic>deep reinforcement learning</topic><topic>distributed proximal policy optimization</topic><topic>Generators</topic><topic>Optimization</topic><topic>Power system dynamics</topic><topic>Security</topic><topic>Stability criteria</topic><topic>Total transfer capability</topic><topic>Training</topic><topic>Transient analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Qiu</creatorcontrib><creatorcontrib>Liu, Youbo</creatorcontrib><creatorcontrib>Zhao, Junbo</creatorcontrib><creatorcontrib>Liu, Junyong</creatorcontrib><creatorcontrib>Chung, Chi Yung</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gao, Qiu</au><au>Liu, Youbo</au><au>Zhao, Junbo</au><au>Liu, Junyong</au><au>Chung, Chi Yung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid Deep Learning for Dynamic Total Transfer Capability Control</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2021-05</date><risdate>2021</risdate><volume>36</volume><issue>3</issue><spage>2733</spage><epage>2736</epage><pages>2733-2736</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2021.3057523</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0001-5257-7280</orcidid><orcidid>https://orcid.org/0000-0002-5465-5243</orcidid><orcidid>https://orcid.org/0000-0001-6607-2240</orcidid><orcidid>https://orcid.org/0000-0002-8498-9666</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0885-8950 |
| ispartof | IEEE transactions on power systems, 2021-05, Vol.36 (3), p.2733-2736 |
| issn | 0885-8950 1558-0679 |
| language | eng |
| recordid | cdi_proquest_journals_2515850037 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | artificial intelligence Deep learning deep reinforcement learning distributed proximal policy optimization Generators Optimization Power system dynamics Security Stability criteria Total transfer capability Training Transient analysis |
| title | Hybrid Deep Learning for Dynamic Total Transfer Capability Control |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T18%3A35%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hybrid%20Deep%20Learning%20for%20Dynamic%20Total%20Transfer%20Capability%20Control&rft.jtitle=IEEE%20transactions%20on%20power%20systems&rft.au=Gao,%20Qiu&rft.date=2021-05&rft.volume=36&rft.issue=3&rft.spage=2733&rft.epage=2736&rft.pages=2733-2736&rft.issn=0885-8950&rft.eissn=1558-0679&rft.coden=ITPSEG&rft_id=info:doi/10.1109/TPWRS.2021.3057523&rft_dat=%3Cproquest_ieee_%3E2515850037%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c295t-167c5813bada7500362a0a1d18a98ba18b504b1d08d457c447c9923373e201843%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2515850037&rft_id=info:pmid/&rft_ieee_id=9349169&rfr_iscdi=true |