{"id":"W2560036223","doi":"10.1109/epec.2016.7771778","title":"Droop gains selection methodology for offshore multi-terminal HVDC networks","year":2016,"lang":"en","type":"article","venue":"","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voltage droop; Converters; Control theory (sociology); Voltage; Transient (computer programming); Steady state (chemistry); Grid; Voltage source; Transmission system; Computer science; Transmission (telecommunications); Stability (learning theory); Engineering; Electronic engineering; Electrical engineering; Mathematics; Telecommunications; Control (management)","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":[0.0005236409,0.0009609331,0.0005116198,0.000886539,0.0003408102,0.0007408049,0.0006897371,0.0003595462,0.002631412],"category_scores_gemma":[0.0008517571,0.0002907219,0.0004457726,0.0004156496,0.0003241162,0.0006160006,0.0005449103,0.0007167862,0.0005839462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961572,"about_ca_system_score_gemma":0.0003472664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007844245,"about_ca_topic_score_gemma":0.001052166,"domain_scores_codex":[0.9996902,0.00007949909,0.00001979255,0.00004980385,0.0001351822,0.00002564781],"domain_scores_gemma":[0.9997444,0.00009365038,0.00004146147,0.00002701749,0.00008326833,0.00001015574],"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":[0.000129072,0.00009173833,0.0005306261,0.000382891,0.00006260833,0.0002910353,0.000182646,0.5254914,0.04641151,0.02361566,0.001064351,0.4017465],"study_design_scores_gemma":[0.00003791135,0.0001917396,0.0003615565,0.00006591326,0.00003483599,0.0001637746,0.00006428974,0.9663865,0.01877602,0.008600519,0.005290043,0.00002686984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003615716,0.0001371282,0.994298,0.00001250418,0.000007538448,0.0000522019,0.000011848,0.0001050097,0.001760043],"genre_scores_gemma":[0.4984254,0.000715788,0.4965509,0.00005155084,0.00004419014,0.0003107298,0.0001224446,0.0001274518,0.003651485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002631412,"threshold_uncertainty_score":0.00880301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09061300124973752,"score_gpt":0.3234243441272306,"score_spread":0.2328113428774931,"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."}}