{"id":"https://openalex.org/W4404036286","doi":"https://doi.org/10.1109/smartgridcomm60555.2024.10738083","title":"A Unified Deep Neural Network for Solving AC OPF in Expanding and Multiple Networks","display_name":"A Unified Deep Neural Network for Solving AC OPF in Expanding and Multiple Networks","publication_year":2024,"publication_date":"2024-09-17","ids":{"openalex":"https://openalex.org/W4404036286","doi":"https://doi.org/10.1109/smartgridcomm60555.2024.10738083"},"language":"en","primary_location":{"id":"doi:10.1109/smartgridcomm60555.2024.10738083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm60555.2024.10738083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100573304","display_name":"Liang Heng","orcid":"https://orcid.org/0009-0002-8205-6747"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Heng Liang","raw_affiliation_strings":["The Chinese University of Hong Kong,Department of Information Engineering,HK,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Department of Information Engineering,HK,China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015970180","display_name":"Changhong Zhao","orcid":"https://orcid.org/0000-0003-0539-8591"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Changhong Zhao","raw_affiliation_strings":["The Chinese University of Hong Kong,Department of Information Engineering,HK,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Department of Information Engineering,HK,China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100602649","display_name":"Minghua Chen","orcid":"https://orcid.org/0000-0002-9983-2936"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Minghua Chen","raw_affiliation_strings":["City University of Hong Kong,School of Data Science,HK,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,School of Data Science,HK,China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16310813,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"575","last_page":"580"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.8260999917984009,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.8260999917984009,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.8163999915122986,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.7979000210762024,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7296258807182312},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6915056705474854},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5264533162117004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4876669645309448}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7296258807182312},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6915056705474854},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5264533162117004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4876669645309448}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smartgridcomm60555.2024.10738083","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smartgridcomm60555.2024.10738083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2062796608","https://openalex.org/W2920303604","https://openalex.org/W3012279112","https://openalex.org/W3039305152","https://openalex.org/W3088315405","https://openalex.org/W3114229415","https://openalex.org/W3115185893","https://openalex.org/W3117855720","https://openalex.org/W3158199598","https://openalex.org/W3200827493","https://openalex.org/W4223635078","https://openalex.org/W4226240952","https://openalex.org/W4285791973","https://openalex.org/W4312570848","https://openalex.org/W4313434053","https://openalex.org/W4366505386","https://openalex.org/W4400447471","https://openalex.org/W6752515464","https://openalex.org/W6767064347","https://openalex.org/W6787541007"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,88],"traditional":[1],"machine":[2],"learning":[3],"models":[4],"to":[5,24,49,115,132],"solve":[6],"optimal":[7],"power":[8,17,26],"flow":[9],"(OPF)":[10],"are":[11,62],"mostly":[12],"trained":[13],"for":[14,53,73],"a":[15,43,92,110],"given":[16,77],"network":[18,47,111],"and":[19,31,70,79,100,106,118],"hence":[20],"lose":[21],"their":[22],"generalizability":[23],"today\u2019s":[25],"networks":[27,60],"with":[28,82,98],"varying":[29,83],"topologies":[30],"growing":[32,101,112],"plug-and-play":[33],"distributed":[34],"energy":[35],"resources":[36],"(DERs).":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,66],"propose":[42],"unified":[44,94],"deep":[45],"neural":[46],"(DNN)":[48],"predict":[50],"the":[51,74,124,128],"solutions":[52,81],"alternating-current":[54],"(AC)":[55],"OPF":[56,80],"problems":[57],"across":[58],"multiple":[59],"that":[61],"successively":[63],"expanding.":[64],"Specifically,":[65],"design":[67],"elastic":[68],"input":[69],"output":[71],"layers":[72],"vectors":[75],"of":[76,103,109,127],"loads":[78],"lengths":[84],"in":[85],"different":[86,99],"networks.":[87],"proposed":[89,129],"method,":[90],"using":[91],"single":[93],"DNN,":[95],"can":[96],"deal":[97],"numbers":[102],"buses,":[104],"loads,":[105],"generators.":[107],"Simulations":[108],"from":[113],"73":[114],"118":[116],"buses":[117],"IEEE":[119],"57/118/300-bus":[120],"test":[121],"systems":[122],"verify":[123],"improved":[125],"performance":[126],"method":[130],"compared":[131],"existing":[133],"methods.":[134]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
