{"id":"https://openalex.org/W7156630803","doi":"https://doi.org/10.48550/arxiv.2604.22784","title":"Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks","display_name":"Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7156630803","doi":"https://doi.org/10.48550/arxiv.2604.22784"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22784","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.22784","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003571812","display_name":"Solon Falas","orcid":"https://orcid.org/0000-0003-1182-4382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Falas, Solon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030299093","display_name":"Markos Asprou","orcid":"https://orcid.org/0000-0002-7553-677X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Asprou, Markos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122965741","display_name":"Charalambos Konstantinou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Konstantinou, Charalambos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063130153","display_name":"Maria K. Michael","orcid":"https://orcid.org/0000-0002-1943-6547"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael, Maria K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10305","display_name":"Power System Optimization and Stability","score":0.49129998683929443,"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/T10305","display_name":"Power System Optimization and Stability","score":0.49129998683929443,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.4106999933719635,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.014000000432133675,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6600000262260437},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6571000218391418},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.586899995803833},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4458000063896179},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.44510000944137573},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4318000078201294},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.3677999973297119}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6600000262260437},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6571000218391418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6392999887466431},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.586899995803833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47609999775886536},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42989999055862427},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C129537906","wikidata":"https://www.wikidata.org/wiki/Q7603913","display_name":"State variable","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3125},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22784","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.22784","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22784","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.8054956197738647}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"State":[0,70],"estimation":[1,40,76],"is":[2,14,93,126,149],"a":[3,16,64,99],"cornerstone":[4],"of":[5,111],"power":[6],"system":[7,132],"control-center":[8],"operations,":[9],"and":[10,26,114,144,159,166],"its":[11],"robust":[12],"operation":[13],"increasingly":[15],"cyber-physical":[17],"security":[18],"concern":[19],"as":[20,34],"modern":[21],"grids":[22],"become":[23],"more":[24],"digitalized":[25],"communication-intensive.":[27],"Neural":[28,43],"network-based":[29],"approaches":[30],"have":[31,54],"gained":[32],"attention":[33],"alternatives":[35],"to":[36,121],"conventional":[37],"model-based":[38],"state":[39,138],"methods.":[41],"Physics-Informed":[42],"Networks":[44],"(PINNs),":[45],"which":[46],"embed":[47],"power-flow":[48],"consistency":[49],"into":[50],"the":[51,75,79,108,129],"learning":[52],"objective,":[53],"shown":[55],"improved":[56],"accuracy":[57,165],"over":[58],"existing":[59,169],"approaches.":[60],"This":[61],"work":[62],"proposes":[63],"PINN-based":[65],"model":[66,92],"for":[67],"Power":[68],"System":[69],"Estimation":[71],"(PSSE)":[72],"that":[73],"protects":[74],"process":[77],"against":[78],"stealth-constrained":[80],"AC":[81],"False":[82],"Data":[83],"Injection":[84],"Attacks":[85],"(FDIAs)":[86],"considered":[87],"in":[88],"this":[89],"study.":[90],"The":[91],"developed":[94],"without":[95],"adversarial":[96],"training.":[97],"Instead,":[98],"dynamic":[100],"loss-weighting":[101],"formulation":[102],"based":[103],"on":[104,128,156],"homoscedastic":[105],"uncertainty":[106],"learns":[107],"relative":[109],"scaling":[110],"supervised":[112],"data-fit":[113],"physics-residual":[115],"terms":[116],"during":[117],"training,":[118],"reducing":[119],"sensitivity":[120],"manual":[122],"weight":[123],"tuning.":[124],"Robustness":[125],"evaluated":[127],"IEEE":[130],"118-bus":[131],"using":[133,151],"representative":[134],"stealthy-FDIA":[135],"families":[136],"including":[137],"distortion,":[139],"load":[140],"redistribution,":[141],"line":[142],"overloading,":[143],"residual-constrained":[145],"stealth":[146],"corruption.":[147],"Performance":[148],"measured":[150],"Mean":[152],"Absolute":[153],"Error":[154],"(MAE)":[155],"voltage":[157],"magnitudes":[158],"phase":[160],"angles.":[161],"Results":[162],"demonstrate":[163],"higher":[164],"stability":[167],"than":[168],"fixed-weight":[170],"PINN":[171],"variants.":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-29T00:00:00"}
