{"id":"W2120143494","doi":"10.1109/jestpe.2013.2294920","title":"A New Power Conversion System for Megawatt PMSG Wind Turbines Using Four-Level Converters and a Simple Control Scheme Based on Two-Step Model Predictive Strategy—Part I: Modeling and Theoretical Analysis","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Emerging and Selected Topics in Power Electronics","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Control theory (sociology); Maximum power point tracking; Boost converter; Power optimizer; Rectifier (neural networks); Controller (irrigation); Engineering; Permanent magnet synchronous generator; Inverter; Converters; Capacitor; Wind power; Power factor; Voltage; Computer science; Electronic engineering; Electrical engineering; 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":[0.0001802881,0.0004948711,0.0005316857,0.0002625926,0.0002993674,0.0006137482,0.0008494755,0.000608556,0.002723141],"category_scores_gemma":[0.0001798242,0.0002613129,0.0003262684,0.0003614281,0.0002586991,0.0007455976,0.000251708,0.0008120036,0.0005010974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003054246,"about_ca_system_score_gemma":0.0002770706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007414956,"about_ca_topic_score_gemma":0.001486596,"domain_scores_codex":[0.9998838,0.0000188808,0.000006931128,0.00002300796,0.00005982712,0.000007560177],"domain_scores_gemma":[0.9999295,0.00002024407,0.0000143222,0.00001142408,0.00001963641,0.000004807416],"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":[0.000244087,0.0003269735,0.001606187,0.001182775,0.0001450855,0.0009381784,0.0002698629,0.4147827,0.1222709,0.04668396,0.005430716,0.4061185],"study_design_scores_gemma":[0.00005757535,0.0003236612,0.0006758979,0.00002989709,0.00003017621,0.0002457353,0.00001886265,0.9779424,0.007016192,0.004480752,0.009160331,0.0000185522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01797549,0.000578893,0.9729261,0.0001568902,0.000137172,0.0001613265,0.00007018357,0.0007531526,0.007240778],"genre_scores_gemma":[0.8521166,0.0008881731,0.1393927,0.0001104723,0.00007886007,0.0003081067,0.0001473769,0.00003509219,0.006922497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002723141,"threshold_uncertainty_score":0.009109795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477137574031622,"score_gpt":0.2368750533616303,"score_spread":0.222103677621314,"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."}}