{"id":"W1522806499","doi":"10.1109/poweri.2006.1632516","title":"GA-identifier and predictive controller for multi-machine power system","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power India Conference","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Identifier; Control theory (sociology); Electric power system; Controller (irrigation); Model predictive control; Transient (computer programming); Computer science; Power (physics); SIGNAL (programming language); Predictive power; Stabilizer (aeronautics); Genetic algorithm; Control engineering; Engineering; Control (management); Artificial intelligence; Physics; Machine learning","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":[0.000586262,0.0003584267,0.0003838734,0.0003345049,0.0002927503,0.0006199855,0.0006537,0.0006955784,0.001143784],"category_scores_gemma":[0.001177804,0.0001488731,0.0002291757,0.0003251152,0.0003810711,0.0003608836,0.0002725373,0.0007898934,0.0002907526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004892402,"about_ca_system_score_gemma":0.0006726047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002952199,"about_ca_topic_score_gemma":0.002914548,"domain_scores_codex":[0.9997496,0.00006247709,0.00001056161,0.00004574463,0.0001086956,0.00002289504],"domain_scores_gemma":[0.9996847,0.000118518,0.00004333626,0.00003789796,0.0001038162,0.00001170884],"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":[0.0000978512,0.00005454489,0.0005607358,0.00009675373,0.00004001717,0.0001433817,0.00006980763,0.8424473,0.0109001,0.01875116,0.001728501,0.1251098],"study_design_scores_gemma":[0.00001172949,0.00004449582,0.0001363587,0.000004885071,0.000005327227,0.00003026254,0.000002795571,0.9956489,0.001411464,0.001547367,0.001151807,0.000004573223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02060767,0.0007316075,0.9723079,0.0001968425,0.0001692053,0.00004786694,0.00002539543,0.001050794,0.004862756],"genre_scores_gemma":[0.8533334,0.0003649412,0.1409989,0.0001250944,0.00005752473,0.0001478675,0.00006131891,0.00004250824,0.00486832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002952199,"threshold_uncertainty_score":0.005869985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118080754717091,"score_gpt":0.2185272238620514,"score_spread":0.2067191483903423,"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."}}