{"id":"https://openalex.org/W7161032193","doi":"https://doi.org/10.1186/s42162-026-00665-2","title":"Intelligent early warning of substation equipment based on GNN network and digital twin","display_name":"Intelligent early warning of substation equipment based on GNN network and digital twin","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161032193","doi":"https://doi.org/10.1186/s42162-026-00665-2"},"language":"en","primary_location":{"id":"doi:10.1186/s42162-026-00665-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-026-00665-2","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s42162-026-00665-2","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111009947","display_name":"Luyao Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]},{"id":"https://openalex.org/I74872605","display_name":"China Southern Power Grid (China)","ror":"https://ror.org/03hkh9419","country_code":"CN","type":"company","lineage":["https://openalex.org/I74872605"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Luyao Pei","raw_affiliation_strings":["Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China","School of Electrical Engineering, Chongqing University, Chongqing, 401331, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China","institution_ids":["https://openalex.org/I74872605"]},{"raw_affiliation_string":"School of Electrical Engineering, Chongqing University, Chongqing, 401331, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136051224","display_name":"Weican Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I74872605","display_name":"China Southern Power Grid (China)","ror":"https://ror.org/03hkh9419","country_code":"CN","type":"company","lineage":["https://openalex.org/I74872605"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weican Yuan","raw_affiliation_strings":["Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China","institution_ids":["https://openalex.org/I74872605"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136088429","display_name":"Cheng Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I74872605","display_name":"China Southern Power Grid (China)","ror":"https://ror.org/03hkh9419","country_code":"CN","type":"company","lineage":["https://openalex.org/I74872605"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Xu","raw_affiliation_strings":["Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China","institution_ids":["https://openalex.org/I74872605"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091134792","display_name":"Chentao Wang","orcid":"https://orcid.org/0000-0002-9973-3313"},"institutions":[{"id":"https://openalex.org/I74872605","display_name":"China Southern Power Grid (China)","ror":"https://ror.org/03hkh9419","country_code":"CN","type":"company","lineage":["https://openalex.org/I74872605"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chentao Wang","raw_affiliation_strings":["Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Development Research Institute, China Southern Power Grid, Guangzhou, 510663, Guangdong, China","institution_ids":["https://openalex.org/I74872605"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111009947"],"corresponding_institution_ids":["https://openalex.org/I158842170","https://openalex.org/I74872605"],"apc_list":{"value":1490,"currency":"USD","value_usd":1490},"apc_paid":{"value":1490,"currency":"USD","value_usd":1490},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42881817,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.12549999356269836,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.12549999356269836,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11941","display_name":"Power System Reliability and Maintenance","score":0.09600000083446503,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12737","display_name":"Electrical Fault Detection and Protection","score":0.08709999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.7365999817848206},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.578000009059906},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5271999835968018},{"id":"https://openalex.org/keywords/early-warning-system","display_name":"Early warning system","score":0.4381999969482422},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42879998683929443},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41830000281333923},{"id":"https://openalex.org/keywords/dependability","display_name":"Dependability","score":0.33709999918937683}],"concepts":[{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.7365999817848206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.642799973487854},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5870000123977661},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.578000009059906},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5271999835968018},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4810999929904938},{"id":"https://openalex.org/C2779296788","wikidata":"https://www.wikidata.org/wiki/Q5326904","display_name":"Early warning system","level":3,"score":0.4381999969482422},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41830000281333923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4000000059604645},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38429999351501465},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3741999864578247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3409999907016754},{"id":"https://openalex.org/C77019957","wikidata":"https://www.wikidata.org/wiki/Q2689057","display_name":"Dependability","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C163164238","wikidata":"https://www.wikidata.org/wiki/Q2737027","display_name":"Failure rate","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C19012869","wikidata":"https://www.wikidata.org/wiki/Q578372","display_name":"Response time","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.259799987077713}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s42162-026-00665-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-026-00665-2","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c45b5ac10bcc489d835f3b34839d90ce","is_oa":false,"landing_page_url":"https://doaj.org/article/c45b5ac10bcc489d835f3b34839d90ce","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Energy Informatics, Vol 9, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s42162-026-00665-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-026-00665-2","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2591981030","https://openalex.org/W2890847874","https://openalex.org/W3165199796","https://openalex.org/W3195668958","https://openalex.org/W3212034051","https://openalex.org/W4214510968","https://openalex.org/W4294805068","https://openalex.org/W4318477598","https://openalex.org/W4388971975","https://openalex.org/W4399345977","https://openalex.org/W4399977345","https://openalex.org/W4402626921","https://openalex.org/W4403333811","https://openalex.org/W4404529364","https://openalex.org/W4406143484","https://openalex.org/W4407360648","https://openalex.org/W4407704642","https://openalex.org/W4408248408","https://openalex.org/W4408528173","https://openalex.org/W4410107195","https://openalex.org/W4410964088","https://openalex.org/W4411807528","https://openalex.org/W4413655272","https://openalex.org/W4413901320","https://openalex.org/W4414014829","https://openalex.org/W4414650110","https://openalex.org/W4414700983","https://openalex.org/W4415890398"],"related_works":[],"abstract_inverted_index":{"To":[0],"solve":[1],"the":[2,46,56,67,87,100,111,139,163,174,213,216,248,277,286,312],"problems":[3],"of":[4,16,59,102,177,215,251,273,279,289,302,307,318],"insufficient":[5],"real-time":[6,185,281],"performance,":[7],"weak":[8,94],"adaptability":[9],"to":[10,119,196,310],"coupling":[11,88],"faults":[12,253],"and":[13,39,66,92,131,151,162,203,231,257,291,300,314],"poor":[14],"reliability":[15,227],"traditional":[17,197],"early":[18,29,114,165,235],"warning":[19,30,115,166,178,236],"methods":[20],"for":[21,184,297],"substation":[22,47],"equipment,":[23],"this":[24,44],"study":[25,284,305],"proposes":[26],"an":[27],"intelligent":[28,298],"model":[31,58,140,217,239],"(GNN-DT)":[32],"which":[33,172],"integrates":[34],"Graph":[35,77],"Neural":[36],"Network":[37],"(GNN)":[38],"Digital":[40],"Twin":[41],"(DT).":[42],"In":[43],"model,":[45],"system":[48,120],"is":[49,62,85,97,122,126,157,168,193,254,306],"abstracted":[50],"as":[51,158,160],"a":[52,76],"weighted":[53],"undirected":[54],"graph,":[55],"physical":[57,108],"key":[60],"equipment":[61],"constructed":[63],"through":[64],"DT,":[65],"parameters":[68],"are":[69],"adaptively":[70],"modified":[71],"by":[72,128],"multi-source":[73],"data":[74,130,265],"fusion.":[75],"Attention":[78],"Mechanism":[79],"(GAT)":[80],"based":[81],"on":[82],"electrical":[83],"characteristics":[84],"designed,":[86],"relationship":[89],"between":[90],"devices":[91],"extract":[93],"abnormal":[95],"features":[96],"strengthened.":[98],"With":[99],"help":[101],"Multi-Task":[103],"Learning":[104],"(MTL)":[105],"framework":[106],"with":[107],"consistency":[109],"constraints,":[110],"dynamic":[112],"hierarchical":[113],"from":[116,218],"device":[117],"level":[118,121,317],"completed.":[123],"The":[124,153,207,238,268,304],"experiment":[125],"verified":[127],"simulation":[129],"real":[132],"operation":[133,299,316],"data.":[134],"Experimental":[135],"results":[136],"show":[137],"that":[138],"achieves":[141],"over":[142,255],"93%":[143],"across":[144],"four":[145],"core":[146],"metrics:":[147],"accuracy,":[148],"precision,":[149,224],"recall,":[150,225],"F1-score.":[152],"false":[154],"positive":[155,229],"rate":[156,250],"low":[159],"3.5%,":[161],"average":[164],"time":[167,180,272],"only":[169],"4.2":[170],"min,":[171],"meets":[173],"engineering":[175,280],"requirement":[176],"response":[179,232],"(<":[181],"100":[182],"ms)":[183],"monitoring":[186],"systems":[187],"in":[188,243,264],"substations.":[189,303],"Its":[190],"overall":[191],"performance":[192],"significantly":[194],"superior":[195],"machine":[198],"learning,":[199,202],"single":[200,204,269],"deep":[201],"GNN/DT":[205],"models.":[206],"above":[208],"evaluation":[209],"indicators":[210],"comprehensively":[211],"verify":[212],"superiority":[214],"three":[219],"dimensions:":[220],"identification":[221],"accuracy":[222],"(accuracy,":[223],"F1-score),":[226],"(false":[228],"rate),":[230],"timeliness":[233],"(average":[234],"time).":[237],"shows":[240],"strong":[241],"robustness":[242],"10":[244],"typical":[245],"fault":[246],"scenarios,":[247],"recall":[249],"hidden":[252],"89%,":[256],"it":[258],"still":[259],"has":[260],"excellent":[261],"generalization":[262],"ability":[263],"scarce":[266],"scenarios.":[267],"sample":[270],"inference":[271],"4.0ms":[274],"can":[275],"meet":[276],"requirements":[278],"deployment.":[282],"This":[283],"realizes":[285],"organic":[287],"integration":[288],"data-driven":[290],"mechanism-driven,":[292],"provides":[293],"reliable":[294],"technical":[295],"solutions":[296],"maintenance":[301],"great":[308],"significance":[309],"improving":[311],"safe":[313],"efficient":[315],"power":[319],"grids.":[320]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-14T00:00:00"}