{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001601203,0.0003764912,0.0005779493,0.0002649982,0.00004964874,0.00003830526,0.000276036,0.0001792945,0.0002577303],"category_scores_gemma":[0.00003330478,0.0003637979,0.0001935458,0.0002382209,0.00002507894,0.0002353798,0.00005346305,0.0002808072,0.0000414484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009306479,"about_ca_system_score_gemma":0.00003019852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001750352,"about_ca_topic_score_gemma":0.00002014081,"domain_scores_codex":[0.9982476,0.00004361751,0.0005284237,0.0003450275,0.0003334247,0.0005018867],"domain_scores_gemma":[0.9991293,0.00005455949,0.000120828,0.0004855937,0.00008186785,0.0001278591],"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":[0.00004594241,0.00003501767,0.001333113,0.0001322193,0.00007196832,0.000007378476,0.0001801955,0.8741943,0.1234367,0.0001718366,0.00005291944,0.0003383555],"study_design_scores_gemma":[0.002237531,0.00007560255,0.00191202,0.0001451218,0.00002917418,0.00001340667,0.00003205876,0.9656942,0.02674586,0.0001005364,0.002552728,0.0004617355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826323,0.001038772,0.003344243,0.0001014004,0.0005748916,0.0001623218,0.00002319461,0.0004256123,0.01169729],"genre_scores_gemma":[0.9974809,0.00001132165,0.000187535,0.00004217713,0.0001898781,0.0000063988,0.00002778466,0.0001363488,0.001917635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09669086,"threshold_uncertainty_score":0.9998814,"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."}}