{"id":"W1938281091","doi":"10.1109/pes.2004.1373081","title":"Synchronous generator model identification using Volterra series","year":2004,"lang":"en","type":"article","venue":"IEEE Power Engineering Society General Meeting, 2004.","topic":"Control Systems and Identification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Volterra series; Generator (circuit theory); Control theory (sociology); Convolution (computer science); Generalization; Nonlinear system; Computer science; Permanent magnet synchronous generator; Voltage; Series (stratigraphy); Volterra integral equation; Applied mathematics; Power (physics); Mathematics; Integral equation; Engineering; Mathematical analysis; Artificial intelligence; Physics","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":[0.0003504441,0.0005309337,0.000506803,0.0004241683,0.0002435108,0.0004878364,0.0005024684,0.0005209983,0.001316829],"category_scores_gemma":[0.0007593818,0.0001771889,0.0004928546,0.0004099282,0.0002011211,0.0006318947,0.0002420198,0.0004633401,0.0006298624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000278041,"about_ca_system_score_gemma":0.0003306674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001675264,"about_ca_topic_score_gemma":0.001337539,"domain_scores_codex":[0.9998159,0.0000577042,0.00001000445,0.00003824362,0.00006707881,0.00001112398],"domain_scores_gemma":[0.9998467,0.00005858018,0.00002267283,0.00002992514,0.00003764993,0.000004435626],"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":[0.00004606034,0.00002886856,0.0004252265,0.00008749591,0.00003442123,0.0001352266,0.00005913386,0.9099734,0.0142061,0.01238336,0.0004649856,0.06215564],"study_design_scores_gemma":[0.000001582819,0.00001206821,0.00008459634,0.000002401977,0.000003153635,0.00002701542,0.000003287939,0.9962015,0.001697324,0.001403288,0.000560753,0.000003110178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01992468,0.0001624852,0.9760672,0.00004346689,0.000032576,0.00003116986,0.00005036394,0.0005422905,0.003145809],"genre_scores_gemma":[0.8797253,0.0004852444,0.113588,0.00003036123,0.0000314172,0.0001278291,0.0001980039,0.00008807258,0.005725658],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001675264,"threshold_uncertainty_score":0.0044052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007923703237206385,"score_gpt":0.2042010222895251,"score_spread":0.1962773190523187,"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."}}