{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001670073,0.0002462814,0.0003104874,0.00009917092,0.000202157,0.00004481469,0.0001142738,0.0001214621,0.00002666913],"category_scores_gemma":[2.921038e-7,0.0002413135,0.00008801032,0.0004199205,0.0000244454,0.0000952095,0.000001257937,0.0001254422,5.593015e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003580444,"about_ca_system_score_gemma":0.00001820862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001631716,"about_ca_topic_score_gemma":0.00007610909,"domain_scores_codex":[0.9989306,0.00004746581,0.000319507,0.0002840934,0.00009436758,0.0003239713],"domain_scores_gemma":[0.9994891,0.00007060026,0.00005044767,0.0002115677,0.00009167173,0.0000866688],"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":[0.0005978902,0.0001315185,0.00002264538,0.0002974695,0.0003782622,0.000001337006,0.00005564791,0.922339,0.0005681081,0.0002102107,0.0003802393,0.07501768],"study_design_scores_gemma":[0.004341784,0.0002320938,0.0004906249,0.0002534202,0.0003692993,4.012444e-7,0.00007190771,0.9909451,0.0003657199,0.0003487806,0.002326686,0.0002541619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003901198,0.001646651,0.9922373,0.0003562397,0.0003244587,0.001082969,0.000118536,0.0001340561,0.0001985952],"genre_scores_gemma":[0.9852189,0.0008886829,0.01304191,0.0004816824,0.0000536727,0.0001958929,0.00006700485,0.00002835592,0.00002383213],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9813178,"threshold_uncertainty_score":0.9840479,"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."}}