{"id":"W2907713664","doi":"10.1109/naps.2018.8600562","title":"A Network-cognizant Aggregate-frequency Reduced-order Power System Dynamical Model","year":2018,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Control theory (sociology); Weighting; Frequency domain; Aggregate (composite); Generator (circuit theory); Inertial frame of reference; Electric power system; Computer science; System dynamics; Power (physics); Frequency response; Time domain; Engineering; Physics; Control (management)","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":[0.0002260441,0.0002166324,0.0002602668,0.00005052635,0.00009258893,0.00005386073,0.0001945132,0.0001516937,0.0003344158],"category_scores_gemma":[0.00002988265,0.0001884863,0.00006215807,0.000345644,0.00006394686,0.0001449084,0.00003989904,0.0001235819,0.0003004467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001596594,"about_ca_system_score_gemma":0.00004329772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001297913,"about_ca_topic_score_gemma":0.00003938172,"domain_scores_codex":[0.9986355,0.00004142119,0.0004062419,0.0002854752,0.000194868,0.0004364596],"domain_scores_gemma":[0.9991007,0.00001986069,0.00003395958,0.0004684973,0.0002129139,0.0001641032],"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":[0.00003804929,0.00009112924,0.001149056,0.0003114011,0.0001541696,0.00001758951,0.001092955,0.9077103,0.0016853,0.05664268,0.0308119,0.0002954942],"study_design_scores_gemma":[0.000218003,0.00002614676,0.0001071521,0.00005902445,0.000009614035,0.00001433437,0.00007224244,0.9986071,0.00009551228,0.0001946376,0.0003448974,0.0002513522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02408818,0.0001327518,0.7416152,0.00007472901,0.001473742,0.0002790437,0.00001230956,0.001413984,0.23091],"genre_scores_gemma":[0.9836497,0.000003750794,0.01528219,0.0000894694,0.0001023524,0.00002459146,0.000009659834,0.0000487677,0.0007895715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9595615,"threshold_uncertainty_score":0.7686248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009309802780134187,"score_gpt":0.210128666216407,"score_spread":0.2008188634362728,"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."}}