{"id":"W2344373970","doi":"10.1109/tpwrd.2015.2499262","title":"A Coherency-Based Equivalence Method for MMC Inverters Using Virtual Synchronous Generator Control","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; University of Toronto; National Natural Science Foundation of China","keywords":"Converters; Equivalence (formal languages); Modular design; Permanent magnet synchronous generator; Control theory (sociology); AC power; Grid; Voltage; Electric power system; Microgrid; Generator (circuit theory); Computer science; Electronic engineering; Power (physics); Engineering; Mathematics; Control (management); Physics; Electrical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003199255,0.0002375229,0.0002691862,0.0001800144,0.0001706628,0.00006583695,0.0001176664,0.00017378,0.0000412716],"category_scores_gemma":[0.000006059008,0.0002466705,0.0001625294,0.0001790901,0.00003768306,0.0002099793,4.416302e-7,0.0001949831,0.00003898958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003538825,"about_ca_system_score_gemma":0.0001643735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002139287,"about_ca_topic_score_gemma":0.0000406753,"domain_scores_codex":[0.9987967,0.00008388114,0.0003086396,0.0002681228,0.0002135148,0.0003291288],"domain_scores_gemma":[0.9992724,0.000101506,0.00004511447,0.0002510675,0.0001436116,0.0001863035],"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":[0.0001531016,0.00006508961,0.000003003916,0.0000482923,0.0001242416,0.000004822692,0.0003061934,0.9246614,0.06102137,0.00001022043,0.0004125386,0.01318975],"study_design_scores_gemma":[0.001990982,0.0004190401,0.00000248744,0.0000651032,0.00008208297,0.00001580058,0.0001449325,0.9595775,0.03579294,0.00001062734,0.001586883,0.000311626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04516433,0.0001163826,0.9510764,0.00001534529,0.002377039,0.0006921223,0.0001518843,0.0003281221,0.00007830716],"genre_scores_gemma":[0.9841247,0.000003426153,0.01529624,0.0001671441,0.00007630329,0.0002226835,0.00000219918,0.00005970463,0.00004755145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9389604,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051307389789703,"score_gpt":0.2609055359281351,"score_spread":0.2303924620302381,"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."}}