{"id":"W2985043180","doi":"10.1109/tec.2019.2951331","title":"An Offshore Wind Farm With DC Collection System Featuring Differential Power Processing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Offshore wind power; Turbine; Wind power; Power optimizer; Marine engineering; Engineering; Wind speed; Electric power system; Automotive engineering; Control theory (sociology); Electrical engineering; Power (physics); Voltage; Computer science; Maximum power point tracking; Meteorology; Inverter; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001578061,0.0003149534,0.0003275237,0.000230448,0.0005101845,0.0003757723,0.0007311605,0.0002720604,0.002505047],"category_scores_gemma":[0.0001976588,0.0002131164,0.000156842,0.0004858167,0.0001717076,0.000296858,0.0002436205,0.0003675958,0.0009727107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002843734,"about_ca_system_score_gemma":0.0003491597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009133745,"about_ca_topic_score_gemma":0.002216929,"domain_scores_codex":[0.9998473,0.00003084703,0.00001128787,0.00003657559,0.0000608252,0.00001314299],"domain_scores_gemma":[0.999809,0.0000320721,0.00002024765,0.00005674367,0.00006168166,0.00002025018],"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":[0.001255831,0.001080736,0.02290144,0.0008985557,0.0001425075,0.002834985,0.0002645753,0.1921498,0.3820631,0.007082938,0.01178514,0.3775403],"study_design_scores_gemma":[0.0007325062,0.006766527,0.03610506,0.0000544964,0.0001562895,0.002436987,0.0002020283,0.7464699,0.1647973,0.003659337,0.03849234,0.0001273404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6454841,0.0001158056,0.3274049,0.00021738,0.0001365851,0.0005775951,0.0007620533,0.003548426,0.02175309],"genre_scores_gemma":[0.9538801,0.00005778097,0.0414644,0.00004373883,0.0000130204,0.0001043415,0.000334488,0.00002903161,0.004072963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002505047,"threshold_uncertainty_score":0.008380175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002547591876687914,"score_gpt":0.1580097182553935,"score_spread":0.1554621263787056,"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."}}