{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002557731,0.0003651218,0.0005411137,0.0001154559,0.00004991758,0.0001395018,0.0001675822,0.0003795385,0.0002541303],"category_scores_gemma":[0.0000871332,0.0003654083,0.0001940114,0.00007760416,0.00002301859,0.00007538989,0.00002477094,0.0003942234,0.00001551427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004191909,"about_ca_system_score_gemma":0.0002729566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001104782,"about_ca_topic_score_gemma":0.00002071739,"domain_scores_codex":[0.9983294,0.0001515931,0.0004732853,0.0004802667,0.0003135256,0.0002519336],"domain_scores_gemma":[0.9987533,0.0001283468,0.00007662927,0.0006276828,0.0002703406,0.000143669],"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":[0.00004011752,0.0000563891,0.00009791325,0.0001929221,0.0001158995,0.000003583119,0.0001434148,0.9985787,0.0001024836,0.00009761187,0.0005084766,0.00006245892],"study_design_scores_gemma":[0.001581903,0.00003318926,0.0002930588,0.00007915639,0.00006091559,4.366433e-7,0.00004654079,0.9974169,0.00009906449,0.00004065062,0.00002671962,0.0003214659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002811817,0.000189449,0.9791733,0.00009098576,0.001074658,0.0009894829,0.0004323551,0.0007334271,0.01450454],"genre_scores_gemma":[0.9936795,0.000009855843,0.005043952,0.0004376569,0.00007007494,0.0003953459,0.0002247104,0.00006003015,0.00007888545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9908677,"threshold_uncertainty_score":0.9998798,"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."}}