{"id":"W4405137644","doi":"10.1049/elp2.12523","title":"Computationally efficient data‐driven model predictive control for modular multilevel converters","year":2024,"lang":"en","type":"article","venue":"IET Electric Power Applications","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Model predictive control; Modular design; Flexibility (engineering); Converters; Computational complexity theory; Computer science; Control theory (sociology); Voltage; Computational model; Control engineering; Control (management); Engineering; Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000153309,0.0001472656,0.0001492935,0.0001373129,0.0001066092,0.00006477838,0.0002368418,0.00008817339,0.00000458771],"category_scores_gemma":[0.00001059687,0.0001505337,0.00005995044,0.0002917824,0.00001580127,0.000106036,0.00001564768,0.000143931,0.0000536919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001449466,"about_ca_system_score_gemma":0.0000753725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006193069,"about_ca_topic_score_gemma":8.925005e-7,"domain_scores_codex":[0.9989781,0.00001160884,0.0002530442,0.0003592541,0.0001626088,0.0002354087],"domain_scores_gemma":[0.9993404,0.0001218271,0.00002524717,0.0003454662,0.00009637377,0.00007067617],"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":[0.000007322385,0.0000332852,0.000001329825,0.000066103,0.0001388466,3.272421e-7,0.0001500937,0.9785954,0.009462796,0.002891756,0.003086132,0.005566619],"study_design_scores_gemma":[0.0003734393,0.00002905349,0.00008790282,0.00001676227,0.00005060183,0.000005997896,0.00001103784,0.9893693,0.0001552983,0.0006725951,0.009072734,0.0001553265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001152105,0.0006630514,0.9937837,0.00008174104,0.0001620079,0.002089209,0.0009758169,0.0007099605,0.0003824229],"genre_scores_gemma":[0.993532,0.0000123191,0.003565975,0.00004029211,0.0001065679,0.002373553,0.000259272,0.00004685953,0.00006314673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9923799,"threshold_uncertainty_score":0.6138588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240528422766975,"score_gpt":0.2495133680122304,"score_spread":0.2371080837845606,"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."}}