{"id":"W4401390959","doi":"10.3390/fractalfract8080463","title":"Voltage Controller Design for Offshore Wind Turbines: A Machine Learning-Based Fractional-Order Model Predictive Method","year":2024,"lang":"en","type":"article","venue":"Fractal and Fractional","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Wind power; Controller (irrigation); Random forest; Offshore wind power; Renewable energy; Computer science; Model predictive control; Turbine; Control theory (sociology); Engineering; Control engineering; Artificial intelligence; Control (management)","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":[0.0005157404,0.0006434798,0.0008039725,0.0003071152,0.0002758755,0.000790925,0.0007329863,0.0007938978,0.001639981],"category_scores_gemma":[0.001223808,0.0003563982,0.0004973965,0.0002883765,0.0002739869,0.0004264322,0.0003755589,0.001016281,0.0003011045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004052953,"about_ca_system_score_gemma":0.0008782136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006837,"about_ca_topic_score_gemma":0.007852277,"domain_scores_codex":[0.9998245,0.00003489096,0.000009908479,0.00004108153,0.00006261444,0.00002716481],"domain_scores_gemma":[0.9995497,0.0002299695,0.00005351876,0.00002191299,0.0001322329,0.00001260595],"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":[0.00002455791,0.00001827027,0.0001616062,0.00004643211,0.00001522604,0.00003696472,0.00002696257,0.9637866,0.001596492,0.001320531,0.0003442945,0.03262199],"study_design_scores_gemma":[0.000001817277,0.00000704063,0.00002280655,0.000002225655,0.000001907234,0.000002414748,0.000001414491,0.9995564,0.0001152392,0.0001882672,0.00009936729,0.000001067525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01217867,0.0003890481,0.9842277,0.0001126386,0.00004444153,0.0000312847,0.0000274146,0.0004866124,0.002502176],"genre_scores_gemma":[0.8807747,0.0003880585,0.1152197,0.0001606765,0.00006039445,0.0001685027,0.0001306425,0.00006492603,0.003032441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01006837,"threshold_uncertainty_score":0.02001953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558916304527434,"score_gpt":0.2499074649991737,"score_spread":0.2343183019538993,"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."}}