{"id":"W3034709036","doi":"10.1109/access.2020.3001076","title":"Dynamic Analysis and Model Order Reduction of Virtual Synchronous Machine Based Microgrid","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Microgrid; Voltage droop; Computer science; Small-signal model; Converters; Permanent magnet synchronous generator; Time domain; Control theory (sociology); Reduction (mathematics); Electric power system; Frequency domain; Stability (learning theory); Inertia; Synchronous motor; Power (physics); Control engineering; Engineering; Control (management); Voltage regulator; Voltage; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000242521,0.0005019851,0.0004260028,0.0003173596,0.0002628203,0.0004773353,0.0003236743,0.0002327738,0.001378991],"category_scores_gemma":[0.0004713936,0.0002005864,0.0004788451,0.0002424288,0.0002860918,0.0003992858,0.0003222708,0.0003669985,0.000181342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003851789,"about_ca_system_score_gemma":0.0004922366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005369707,"about_ca_topic_score_gemma":0.004058374,"domain_scores_codex":[0.9999026,0.0000349715,0.000004616033,0.00001529112,0.00003116755,0.00001135943],"domain_scores_gemma":[0.9998792,0.00004897831,0.00002464581,0.00001555979,0.00002658157,0.000004906473],"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":[0.00001854562,0.00000970063,0.0003826625,0.00004015795,0.0000112785,0.00004403024,0.00003870716,0.9837794,0.002273651,0.005107862,0.0002124262,0.008081627],"study_design_scores_gemma":[0.000001148011,0.00001013747,0.0001002479,0.000001366725,0.0000020533,0.000005043025,0.000004675843,0.9987122,0.0002001257,0.0007813108,0.0001804855,0.000001264596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063426,0.0004687831,0.8823208,0.0001664486,0.00003297781,0.00005029316,0.0001283328,0.0005200417,0.009969639],"genre_scores_gemma":[0.9824435,0.0002450487,0.01486666,0.00001847508,0.00001129903,0.00005164542,0.0001031856,0.00004279348,0.002217578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005369707,"threshold_uncertainty_score":0.01067686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006311494553901328,"score_gpt":0.218432071002698,"score_spread":0.2121205764487967,"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."}}