{"id":"W4237343010","doi":"10.32920/ryerson.14663655","title":"Synchronous Generator Excitation Control Based on Model Predictive Control","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Model predictive control; Generator (circuit theory); Control theory (sociology); Excitation; Computer science; Control (management); Optimal control; Mathematical optimization; Mathematics; Engineering; Power (physics); Artificial intelligence; Physics; Electrical 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":[0.0002805641,0.0003987061,0.0003537507,0.0002140569,0.0001958918,0.0005992645,0.0004497691,0.0002977512,0.001406443],"category_scores_gemma":[0.0005506579,0.0001449569,0.0001923714,0.0002733143,0.0003341833,0.0004362502,0.0002736729,0.0004349596,0.0002700303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003169284,"about_ca_system_score_gemma":0.0004578887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605372,"about_ca_topic_score_gemma":0.001588744,"domain_scores_codex":[0.9997937,0.00004804579,0.000006771535,0.00003874811,0.00009138164,0.00002131854],"domain_scores_gemma":[0.9998716,0.00004852847,0.00001858291,0.00001797347,0.00003746787,0.000005886986],"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":[0.00004900722,0.00003568389,0.0002438526,0.00006541388,0.000014678,0.00005219683,0.00003271416,0.8876416,0.007766524,0.03332458,0.001110695,0.06966304],"study_design_scores_gemma":[0.000005720514,0.00002623656,0.00006746558,0.00000238194,0.000002812541,0.000007466944,0.000001847775,0.9955035,0.001635512,0.002018033,0.0007268835,0.000002148299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02558694,0.0002508164,0.9593888,0.000121341,0.00006275913,0.0000316023,0.00003323216,0.0007011367,0.01382326],"genre_scores_gemma":[0.9565964,0.0002409704,0.03873559,0.00003538403,0.00003868029,0.00005107498,0.0000580877,0.00004395953,0.004199888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001605372,"threshold_uncertainty_score":0.004704952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008019850353280377,"score_gpt":0.204140350683389,"score_spread":0.1961205003301086,"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."}}