{"id":"W2948555760","doi":"10.1109/tpwrs.2019.2920782","title":"A Self-Adjusting Adaptive AVR-LFC Scheme for Synchronous Generators","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université de Moncton","funders":"","keywords":"Robustness (evolution); Control theory (sociology); Automatic Generation Control; Electric power system; Nonlinear system; Computer science; Voltage regulator; Heuristics; PID controller; Voltage; Control engineering; Adaptive control; Mathematical optimization; Power (physics); Engineering; Control (management); Mathematics; Temperature control","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":[0.0002678616,0.000425952,0.0004265164,0.0003746022,0.0003393337,0.0004004784,0.001015991,0.0004756387,0.001320844],"category_scores_gemma":[0.0003953814,0.000120829,0.0002932668,0.0003413713,0.0002590244,0.0003605113,0.0003263844,0.0004514361,0.0002804486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713542,"about_ca_system_score_gemma":0.000262521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378117,"about_ca_topic_score_gemma":0.002037153,"domain_scores_codex":[0.999859,0.00003534415,0.00001069372,0.00002825702,0.00005020189,0.00001646506],"domain_scores_gemma":[0.999873,0.00003478553,0.00002864405,0.00001611076,0.00003817493,0.000009354857],"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":[0.0001493087,0.0001145624,0.0004483465,0.0002134706,0.00008269693,0.0001728006,0.0001470664,0.543053,0.04501063,0.01375642,0.002423163,0.3944286],"study_design_scores_gemma":[0.00002005748,0.0000888615,0.0001168296,0.00000625271,0.000009168739,0.00004081048,0.00000423667,0.9957088,0.001993061,0.0007569676,0.00124659,0.000008296776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02539188,0.0004136709,0.9676344,0.00007724561,0.00008874753,0.0000612221,0.00002573377,0.0006102197,0.005696805],"genre_scores_gemma":[0.9113083,0.0001434121,0.08593691,0.00006058995,0.00004220021,0.00008376114,0.00003676383,0.00003415216,0.002353898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001378117,"threshold_uncertainty_score":0.004418671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00854861892057248,"score_gpt":0.1976850389242274,"score_spread":0.1891364200036549,"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."}}