{"id":"W4390416710","doi":"10.1109/ecce53617.2023.10362116","title":"Neural-Predictor-based Data-Driven Predictive Control for 100kVA, 4.16/0.48 kV Solid-State Transformer for Smart Distribution Grids","year":2023,"lang":"en","type":"article","venue":"","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Control theory (sociology); Robustness (evolution); Smart grid; Model predictive control; Discretization; Artificial neural network; Computer science; Transformer; Nonlinear system; Voltage; Electric power system; Control engineering; Electronic engineering; Engineering; Power (physics); Mathematics; Artificial intelligence; Control (management); Electrical 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":[0.0004131203,0.0004029244,0.000408779,0.000175268,0.0003387733,0.0006041618,0.0006968902,0.0003478263,0.001133254],"category_scores_gemma":[0.0005299493,0.0002170168,0.000215411,0.0002562272,0.0004025752,0.0003914096,0.0004188965,0.0007527296,0.0001347234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006216887,"about_ca_system_score_gemma":0.0008208401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009105751,"about_ca_topic_score_gemma":0.01272846,"domain_scores_codex":[0.9998544,0.00002542234,0.000009491085,0.00002934852,0.00006232499,0.00001883638],"domain_scores_gemma":[0.9998191,0.00006268083,0.00003076346,0.00001126617,0.00006683893,0.000009420404],"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":[0.00008635212,0.00006258463,0.0004903208,0.00007849631,0.00002488448,0.00005920903,0.00004312978,0.9347968,0.004431339,0.00258136,0.0009693811,0.05637627],"study_design_scores_gemma":[0.000005022397,0.00002683064,0.00006162987,0.000001869719,0.00000338814,0.000003707927,0.000002282554,0.9989617,0.0006042429,0.0001934429,0.0001339906,0.000001873964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08313298,0.0006007529,0.9074773,0.000324669,0.0001595594,0.00008727796,0.00006567454,0.0009071833,0.007244736],"genre_scores_gemma":[0.9837412,0.0001318684,0.01437872,0.0000352866,0.00001666093,0.00005608898,0.00003592256,0.000009543109,0.001594753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009105751,"threshold_uncertainty_score":0.01810551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477174934335307,"score_gpt":0.2402636640757331,"score_spread":0.22549191473238,"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."}}