{"id":"W1990733967","doi":"10.1109/ecce.2013.6647059","title":"Maximum power point tracking algorithm with advanced state detection and regression method for small wind energy systems","year":2013,"lang":"en","type":"article","venue":"","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Wind speed; Wind power; Transient (computer programming); Control theory (sociology); Computer science; Power (physics); Maximum power principle; Tracking (education); Energy (signal processing); Regression; Set (abstract data type); Maximum power point tracking; Point (geometry); Regression analysis; Electric power system; Oscillation (cell signaling); Algorithm; State (computer science); Rotational speed; Steady state (chemistry); Engineering; Artificial intelligence; Mathematics; Machine learning; Statistics; Meteorology","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":[0.0004766826,0.000417407,0.000543177,0.0005845992,0.0002962247,0.0004510709,0.0006191036,0.0004266328,0.001466616],"category_scores_gemma":[0.001389837,0.0002668017,0.0003105803,0.0005525208,0.0002227404,0.0007203395,0.0003437266,0.000673417,0.0005314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002504951,"about_ca_system_score_gemma":0.0004538216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038256,"about_ca_topic_score_gemma":0.001581185,"domain_scores_codex":[0.9997399,0.00006075733,0.00001669099,0.00006302093,0.0001028042,0.00001670895],"domain_scores_gemma":[0.9996277,0.0001785651,0.00005918216,0.0000334208,0.00008967023,0.00001139889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001591204,0.00009083369,0.0009617342,0.00009562253,0.00003757611,0.00007873545,0.00009359951,0.3219032,0.02497018,0.006643989,0.001722308,0.6432431],"study_design_scores_gemma":[0.000009541864,0.00003733636,0.0002632886,0.000003639105,0.000004144289,0.00002678771,0.000003767388,0.9953592,0.002489079,0.0008181657,0.0009786064,0.000006289081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00712278,0.00009998652,0.9914054,0.00002912606,0.00001267131,0.0000177576,0.000009879484,0.000726809,0.0005755814],"genre_scores_gemma":[0.3425882,0.0002075558,0.6538063,0.0000371095,0.00003475539,0.0001390448,0.0001099226,0.0001011629,0.00297592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002038256,"threshold_uncertainty_score":0.004906297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005847763420429198,"score_gpt":0.1986115667537201,"score_spread":0.1927638033332909,"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."}}