{"id":"W2968071386","doi":"10.1109/tpel.2019.2933770","title":"Multirate Harmonic Compensation Control for Low Switching Frequency Converters: Scheme, Modeling, and Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Converters; Control theory (sociology); Harmonics; Interfacing; Harmonic; Compensation (psychology); Sampling (signal processing); Computer science; Automatic frequency control; Describing function; Harmonic analysis; Electronic engineering; Engineering; Voltage; Filter (signal processing); Control (management); Nonlinear system; Telecommunications; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001258471,0.0002073445,0.0002941425,0.0002353675,0.0001033611,0.00006764699,0.00008524491,0.0001155636,0.00006735051],"category_scores_gemma":[0.000001707127,0.0002205391,0.0001677893,0.0002603338,0.000008808145,0.0002299434,3.399598e-7,0.0002587272,0.00001819311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384204,"about_ca_system_score_gemma":0.00003407049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000135927,"about_ca_topic_score_gemma":0.00006503627,"domain_scores_codex":[0.9989772,0.00002054722,0.0002779559,0.000264946,0.0001099201,0.0003494301],"domain_scores_gemma":[0.9995418,0.0000555332,0.00004327021,0.0002098263,0.00008633683,0.00006320869],"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":[0.00005661376,0.00003487922,0.00003907024,0.00002984523,0.0006718755,2.037279e-7,0.0001346741,0.9599567,0.03590814,0.00008041853,0.000004883828,0.003082693],"study_design_scores_gemma":[0.001968889,0.0001232622,0.00001654888,0.00001288621,0.0003507663,0.000001395444,0.00001919622,0.9924248,0.004688661,0.00009934967,0.00005765856,0.0002365884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1617654,0.0008133695,0.8364456,0.00004411307,0.0002151477,0.000470113,0.00003459294,0.0001764672,0.00003520796],"genre_scores_gemma":[0.9971409,0.0004474898,0.00212894,0.000116598,0.00001063627,0.00006312085,0.00002111515,0.00004112298,0.00003004055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8353755,"threshold_uncertainty_score":0.8993322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004726341219784197,"score_gpt":0.1943914824123024,"score_spread":0.1896651411925182,"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."}}