{"id":"W2986838648","doi":"10.1002/we.2440","title":"Machine learning–based piecewise affine model of wind turbines during maximum power point tracking","year":2019,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Nonlinear system; Wind power; Maximum power point tracking; Cluster analysis; Aerodynamics; Turbine; Piecewise; Wind tunnel; Affine transformation; Operating point; Computer science; Engineering; Power (physics); Artificial intelligence; Mathematics; Physics; Electronic engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002066781,0.0004420748,0.0004440526,0.0002524646,0.0002908926,0.0005223667,0.0005605189,0.0004224771,0.001358969],"category_scores_gemma":[0.0005804489,0.0002658921,0.0004440037,0.0002747893,0.000348104,0.0004563144,0.0002908818,0.0005321471,0.000255191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323115,"about_ca_system_score_gemma":0.0002837627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006233509,"about_ca_topic_score_gemma":0.00330545,"domain_scores_codex":[0.9998947,0.00002244751,0.000005464023,0.00003408673,0.00002809133,0.00001512852],"domain_scores_gemma":[0.9998254,0.00006015872,0.00004766182,0.00001494088,0.0000426281,0.000009198264],"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":[0.00002713276,0.000008794314,0.0004949442,0.00002536077,0.00001185866,0.00007723215,0.00002735091,0.9895871,0.002924251,0.001224613,0.0001105357,0.005480904],"study_design_scores_gemma":[8.027265e-7,0.000008811632,0.0001935737,9.266928e-7,0.00000177991,0.000007774325,0.000002085773,0.9994212,0.0001448409,0.0001432059,0.00007374063,0.0000012773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.156735,0.0003201294,0.8345726,0.0001978605,0.00005598476,0.00006453444,0.0001786335,0.0005281561,0.007347127],"genre_scores_gemma":[0.9928095,0.00009355363,0.00478411,0.000009382995,0.000006253964,0.00003151883,0.00004886175,0.00001360754,0.002203115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006233509,"threshold_uncertainty_score":0.01239449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0056879675974319,"score_gpt":0.1688860628252069,"score_spread":0.163198095227775,"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."}}