{"id":"W2548566047","doi":"10.1109/iecon.2008.4758284","title":"Adaptive algorithm for fast maximum power point tracking in wind energy systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Wind power; Computer science; Maximum power principle; Power (physics); Renewable energy; Range (aeronautics); Algorithm; Rotor (electric); Climb; Maximum power point tracking; Energy (signal processing); Wind speed; Electric power system; Engineering; Electrical engineering; Mathematics","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":[0.0001117728,0.0001439383,0.0001827072,0.0001597812,0.00005233357,0.00002444082,0.0001023489,0.00007586653,0.00005417033],"category_scores_gemma":[0.000007321782,0.000129587,0.00004676944,0.0001600694,0.00002011241,0.0001459093,0.00002303628,0.00008941741,0.00001599238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429161,"about_ca_system_score_gemma":0.00004431742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002243389,"about_ca_topic_score_gemma":0.00003743245,"domain_scores_codex":[0.9989589,0.00001333148,0.0002193815,0.0001689051,0.0001905717,0.0004488956],"domain_scores_gemma":[0.9996565,0.00004533403,0.00001113807,0.0001142196,0.00004973953,0.0001230992],"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":[0.0001454566,0.0003525285,0.001043042,0.0001271949,0.0005481479,0.0009578617,0.00410664,0.5722886,0.001602716,0.008816231,0.04983052,0.360181],"study_design_scores_gemma":[0.00180459,0.0002003295,0.003858095,0.00009535962,0.00000332032,0.0001392897,0.001422225,0.9507232,0.005298889,0.0003898686,0.0353838,0.0006810229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01350075,0.0007178381,0.9449341,0.00005547185,0.0005759173,0.0002603403,0.00001806817,0.0002838039,0.03965366],"genre_scores_gemma":[0.9881694,0.00006593149,0.009538232,0.00002962755,0.00009096575,0.00006876054,0.00001223546,0.00003672456,0.001988098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9746687,"threshold_uncertainty_score":0.5284406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840172432368966,"score_gpt":0.2172402604407013,"score_spread":0.1988385361170116,"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."}}