{"id":"W3200781978","doi":"10.1109/tste.2021.3094093","title":"Maximum Power Tracking for a Wind Energy Conversion System Using Cascade-Forward Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Control theory (sociology); Cascade; Wind power; Controller (irrigation); Artificial neural network; Power (physics); Computer science; Power optimizer; Maximum power point tracking; Engineering; Electricity generation; Control engineering; Power control; Voltage; Control (management); Artificial intelligence; Electrical 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":[0.0001940967,0.0004250162,0.0005696356,0.0003039161,0.0004203056,0.0001749694,0.0001932475,0.0003433809,0.00004788795],"category_scores_gemma":[0.000007295476,0.0004706502,0.0003836722,0.0006108368,0.00003165856,0.0004210721,0.000003510589,0.0002662679,0.000001829814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000950855,"about_ca_system_score_gemma":0.000110564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006309095,"about_ca_topic_score_gemma":0.00006408033,"domain_scores_codex":[0.9975901,0.000109057,0.0005420755,0.0004802813,0.0002889617,0.0009895712],"domain_scores_gemma":[0.9986482,0.0001943537,0.00008705593,0.0004647388,0.0003834578,0.0002222501],"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":[0.00007797986,0.00004251927,0.000002320533,0.0002992331,0.0002039511,0.0003242469,0.00008600436,0.9931991,0.002059929,0.001221512,0.0002766854,0.002206479],"study_design_scores_gemma":[0.001511991,0.00008638963,0.000004133084,0.0001067472,0.0001421176,0.0002550288,0.002755123,0.946199,0.030033,0.00003771848,0.01836012,0.0005086453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01876421,0.0009811119,0.9758011,0.00004919119,0.002791507,0.0002163519,0.0000217897,0.0005324007,0.000842403],"genre_scores_gemma":[0.9962652,0.00002296816,0.0001671494,0.00009246023,0.0003393593,0.00009251014,0.00001367341,0.0001625452,0.002844181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.977501,"threshold_uncertainty_score":0.9997745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008091913006225174,"score_gpt":0.1994808234553309,"score_spread":0.1913889104491057,"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."}}