{"id":"W4413212556","doi":"10.1109/tnsm.2025.3594954","title":"An Edge-Based Adaptive Event-Triggered Network Transmission Scheme for Fully Distributed Power and Frequency Control of Islanded AC Microgrids","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Sichuan Association for Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Scheme (mathematics); Transmission (telecommunications); Automatic frequency control; Power control; Enhanced Data Rates for GSM Evolution; Transmission network; Power (physics); Electronic engineering; Computer network; Distributed computing; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017622,0.0002515463,0.00021458,0.0001201964,0.000198878,0.0002848909,0.0005444805,0.000199555,0.0008756617],"category_scores_gemma":[0.0003435966,0.00006011904,0.0001253098,0.0001273619,0.0002128411,0.0004270576,0.0003618044,0.0002313363,0.00008914976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433468,"about_ca_system_score_gemma":0.0001670462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006405294,"about_ca_topic_score_gemma":0.0009018286,"domain_scores_codex":[0.9998974,0.00002477583,0.000008905248,0.0000279856,0.00002841305,0.00001260127],"domain_scores_gemma":[0.9998338,0.00003924979,0.00003609373,0.00003411714,0.00004249726,0.00001408839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005502948,0.0001768757,0.0007692401,0.0001176703,0.00004293446,0.0002849156,0.0002305391,0.6832427,0.09437283,0.02891663,0.001721592,0.1895737],"study_design_scores_gemma":[0.00001209719,0.00010012,0.0001861912,0.000002373744,0.000005003548,0.00002982985,0.000006955117,0.9936962,0.003743636,0.001808836,0.0004028205,0.000005940081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1126058,0.00007549354,0.8828297,0.00005997137,0.00003784238,0.00004217128,0.0000371676,0.0004413916,0.003870529],"genre_scores_gemma":[0.9857242,0.0000209979,0.01366559,0.00001391563,0.00000640315,0.00001401915,0.00001512958,0.000006590475,0.0005332583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008756617,"threshold_uncertainty_score":0.00292933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004024045374749316,"score_gpt":0.1976887246099013,"score_spread":0.193664679235152,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}