{"id":"W2063932628","doi":"10.1109/tpel.2013.2292068","title":"Predictive Control of a Three-Level Boost Converter and an NPC Inverter for High-Power PMSG-Based Medium Voltage Wind Energy Conversion Systems","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":349,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maximum power point tracking; Boost converter; Inverter; Rectifier (neural networks); Engineering; Permanent magnet synchronous generator; Electronic engineering; Topology (electrical circuits); Converters; Power optimizer; Grid-tie inverter; Control theory (sociology); Electrical engineering; Voltage; Computer science; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002127028,0.000437002,0.0005692373,0.00024465,0.0001354033,0.00005024515,0.0002391744,0.0003166065,0.00009045369],"category_scores_gemma":[0.000003883863,0.0004259287,0.0001585989,0.0001310611,0.0001461246,0.0002956603,0.000001508872,0.000313695,0.0000063925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001929816,"about_ca_system_score_gemma":0.000108486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001499572,"about_ca_topic_score_gemma":0.0001674218,"domain_scores_codex":[0.9980671,0.00006562072,0.0004729833,0.000461802,0.0003224234,0.000610001],"domain_scores_gemma":[0.9988027,0.0002215567,0.000104636,0.000461772,0.0001717829,0.0002375181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01372945,0.004342871,0.001649639,0.003602833,0.01006398,0.00003270951,0.007488159,0.3845173,0.4450205,0.01675805,0.02051795,0.09227661],"study_design_scores_gemma":[0.004808547,0.00162078,0.0001871528,0.00007702671,0.0001943203,0.000005337329,0.00008454694,0.9469917,0.04322545,0.0001111962,0.002233941,0.0004600177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01738602,0.0001802017,0.9795478,0.00007354735,0.001567566,0.0005697114,0.0004185189,0.0001797523,0.00007690017],"genre_scores_gemma":[0.9988716,0.00003722662,0.00008336414,0.000634506,0.00004188624,0.0001225429,0.00002679175,0.0001025997,0.00007948709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9814855,"threshold_uncertainty_score":0.9998193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00822500414450679,"score_gpt":0.1937414287495998,"score_spread":0.185516424605093,"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."}}