{"id":"W2979733152","doi":"10.1109/ccece.2019.8861937","title":"Frequency Scan Based Stability Analysis of an LCC-HVdc System","year":2019,"lang":"en","type":"article","venue":"","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Nyquist stability criterion; Grid; Stability (learning theory); Frequency domain; Electric power system; Computer science; SIGNAL (programming language); Control theory (sociology); Nyquist–Shannon sampling theorem; Nyquist frequency; HVDC converter; Electronic engineering; Power (physics); Engineering; Voltage; Electrical engineering; Telecommunications; Mathematics; Physics; Transformer; Bandwidth (computing); Artificial intelligence","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":[0.0001520451,0.0002440307,0.0002445679,0.0003402493,0.0001810288,0.0003088933,0.0001738169,0.0002321829,0.002006617],"category_scores_gemma":[0.0004550211,0.00008861013,0.0001749215,0.0002101286,0.00026873,0.000270158,0.0001917876,0.0002088944,0.0001965867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002465984,"about_ca_system_score_gemma":0.0002301284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125378,"about_ca_topic_score_gemma":0.001510836,"domain_scores_codex":[0.9999042,0.00002059245,0.000003547003,0.00001748409,0.00004602943,0.000008194775],"domain_scores_gemma":[0.9998599,0.00007032276,0.00002281955,0.000008703462,0.00003424679,0.000003998935],"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":[0.0002289189,0.00004359836,0.003769831,0.0002156242,0.00006657536,0.000429184,0.0002951514,0.7987659,0.1094307,0.01653509,0.000755638,0.06946386],"study_design_scores_gemma":[0.000001522534,0.0000259895,0.0008312869,0.000004478888,0.000003821756,0.00003232805,0.0000210622,0.9951913,0.002861041,0.0007208027,0.0003023555,0.000004005487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2755387,0.0003601058,0.7126489,0.0001092537,0.00001531964,0.00006618676,0.0001324189,0.0003159052,0.0108132],"genre_scores_gemma":[0.9820124,0.000191642,0.01541103,0.00001044577,0.000006135621,0.00003282024,0.00006504585,0.0000222621,0.002248227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002125378,"threshold_uncertainty_score":0.006712794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007137425556116422,"score_gpt":0.1942396135456065,"score_spread":0.18710218798949,"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."}}