{"id":"W3160433831","doi":"10.1109/tia.2021.3079380","title":"Developing and Testing Model Predictive Control to Minimize Ground Potentials in Transformerless Interconnected Five-Level Power Electronic Converters","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Converters; Model predictive control; Control theory (sociology); Power (physics); Grid; Capacitor; Engineering; Steady state (chemistry); Electronic engineering; Computer science; Electrical engineering; Control (management); Voltage; Mathematics; Physics","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":[0.00008898781,0.0002721269,0.0002970506,0.0002143126,0.0001590994,0.00006198412,0.0001578553,0.0003129765,0.00005703832],"category_scores_gemma":[0.000006108852,0.0003222373,0.00006308907,0.0005866843,0.00005333926,0.0002077474,0.000001832062,0.0007643017,0.00001737985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003325165,"about_ca_system_score_gemma":0.0001790084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004704321,"about_ca_topic_score_gemma":0.00008433831,"domain_scores_codex":[0.9985285,0.00003690032,0.000410711,0.0004229354,0.0001429686,0.0004579863],"domain_scores_gemma":[0.9992701,0.0001654921,0.00003188622,0.0002484951,0.0001169306,0.0001670863],"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":[0.0001324427,0.0002849327,0.0001684498,0.0001328495,0.0005115065,0.00001451946,0.003146409,0.7779558,0.04728761,0.0007370858,0.0001270867,0.1695013],"study_design_scores_gemma":[0.001559814,0.0000378758,0.00176288,0.00013072,0.00007824419,0.00003369149,0.001654847,0.9792098,0.01466342,0.0003071653,0.00009802607,0.000463516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06613545,0.00002882068,0.9319797,0.0004408287,0.0001146461,0.0006443901,0.0002255007,0.0001748836,0.0002557669],"genre_scores_gemma":[0.9945577,0.00002216148,0.003524531,0.000605272,0.00001637368,0.001088002,0.000009452833,0.00004695874,0.0001295219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9284552,"threshold_uncertainty_score":0.999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03052246296317777,"score_gpt":0.2439205747894425,"score_spread":0.2133981118262648,"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."}}