{"id":"W4407086267","doi":"10.3390/wind5010004","title":"Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance","year":2025,"lang":"en","type":"article","venue":"Wind","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wind power; Turbine; Automotive engineering; Fuzzy logic; Renewable energy; Computer science; Pitch control; Variable speed wind turbine; Electric power system; Control theory (sociology); Wind speed; Power (physics); Engineering; Control (management); Electrical engineering; Permanent magnet synchronous generator; Meteorology; Aerospace 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":[],"consensus_categories":[],"category_scores_codex":[0.0001033475,0.0001434761,0.0001540455,0.00005022472,0.0001378018,0.00003980565,0.00006521083,0.00007791593,0.00001158165],"category_scores_gemma":[0.00001824422,0.0001330757,0.00003555244,0.0001084998,0.00003142024,0.000178966,0.00001624734,0.0001013547,0.000002836444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003089273,"about_ca_system_score_gemma":0.00001457756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002996465,"about_ca_topic_score_gemma":0.00000182831,"domain_scores_codex":[0.9993526,0.000008542568,0.0001640876,0.0001726278,0.00006513388,0.0002370447],"domain_scores_gemma":[0.9997677,0.00004813032,0.00002373653,0.00009753164,0.00003586929,0.00002703411],"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":[0.00007839985,0.00002334534,0.0006040828,0.00006871208,0.00007958068,0.000002875214,0.0009297241,0.9720141,0.009714187,0.01077001,0.0005376716,0.005177278],"study_design_scores_gemma":[0.001821132,0.0002813873,0.001597693,0.0001120002,0.00004948218,0.000009377318,0.0001114226,0.9820474,0.00770976,0.000270563,0.005671419,0.000318363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8681858,0.001654835,0.1133149,0.00007875977,0.0006295483,0.0002420143,0.0000155257,0.0001107141,0.01576782],"genre_scores_gemma":[0.9944313,0.00003193525,0.004997337,0.0001306448,0.0001474136,0.000008985558,0.00001060307,0.00001597788,0.0002258654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1262454,"threshold_uncertainty_score":0.542667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633774895778148,"score_gpt":0.2204841617018803,"score_spread":0.2041464127440988,"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."}}