{"id":"W3214778315","doi":"10.1016/j.ijepes.2021.107596","title":"The use of analytical converter loss formula to eliminate DC slack/droop bus iteration in sequential AC-DC power flow algorithm","year":2021,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voltage droop; Converters; Algorithm; Slack bus; Power (physics); Computer science; Grid; Iterative method; AC power; Power flow; Control theory (sociology); Mathematics; Electrical engineering; Voltage; Electric power system; Power-flow study; Engineering; Voltage source; Physics","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":[0.0006878488,0.0005020318,0.0003862401,0.0004579515,0.000454025,0.0007041321,0.0007266842,0.0002589767,0.003868171],"category_scores_gemma":[0.002460446,0.0002557361,0.0002630828,0.0004482754,0.0003160953,0.0008513348,0.0004415344,0.0008115631,0.0006266898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004236694,"about_ca_system_score_gemma":0.001385303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003420467,"about_ca_topic_score_gemma":0.005155026,"domain_scores_codex":[0.999764,0.00006686081,0.00001442049,0.0000270646,0.0001030995,0.00002454016],"domain_scores_gemma":[0.9995121,0.0001751807,0.00002958344,0.00006416917,0.0002039288,0.00001501707],"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":[0.0001859852,0.0001888216,0.001363428,0.0001276355,0.00002784311,0.0000975648,0.000132064,0.5014033,0.01254842,0.07288605,0.004487786,0.4065511],"study_design_scores_gemma":[0.000008684241,0.00001983597,0.00008852952,0.000005128401,0.000003798966,0.00001866583,0.00000523806,0.9938221,0.001948403,0.003226089,0.0008511248,0.000002286441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005751522,0.00003886812,0.9917291,0.00004779826,0.0000413329,0.00003303946,0.00001126069,0.0002470242,0.00210014],"genre_scores_gemma":[0.3308034,0.0001208639,0.663394,0.00008136929,0.00003253081,0.0001625359,0.00009054229,0.0002490123,0.005065843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003868171,"threshold_uncertainty_score":0.01294029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147232799358209,"score_gpt":0.2479254017996363,"score_spread":0.2332021218638154,"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."}}