{"id":"W2921730244","doi":"10.48550/arxiv.1903.07502","title":"Investigating the Impacts of Stochastic Load Fluctuation on Dynamic Voltage Stability Margin Using Bifurcation Theory","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Control theory (sociology); Margin (machine learning); Monte Carlo method; Bifurcation; Work (physics); Stability (learning theory); Voltage; Stochastic differential equation; Electric power system; Power (physics); Mechanics; Statistical physics; Mathematics; Computer science; Applied mathematics; Physics; Nonlinear system; Statistics; Thermodynamics","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":[0.0005447333,0.0002489919,0.0003135914,0.0004094585,0.0002012978,0.0004998337,0.0001597348,0.0002678314,0.0006732771],"category_scores_gemma":[0.003866922,0.0001257794,0.0002481385,0.0002479389,0.0004814595,0.0006903724,0.0003843047,0.0003331057,0.00005743041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003207961,"about_ca_system_score_gemma":0.000235546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007315787,"about_ca_topic_score_gemma":0.0004154381,"domain_scores_codex":[0.9998323,0.00006422003,0.000006875301,0.00002576799,0.00004832696,0.00002246806],"domain_scores_gemma":[0.9981824,0.001395025,0.0002330304,0.00005957599,0.00008845555,0.0000415836],"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":[0.0001060328,0.00003626203,0.006817916,0.00007692119,0.0000569253,0.0002584103,0.0001224328,0.9219847,0.02276672,0.03159353,0.0002924438,0.01588775],"study_design_scores_gemma":[0.000002134349,0.00002451403,0.001524413,0.000003314006,0.000006656723,0.00002417782,0.00001513552,0.9918269,0.001747306,0.00470808,0.0001111746,0.000006181348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7991024,0.000555914,0.1939882,0.0003113431,0.00002961069,0.0000199133,0.00005029878,0.00009549629,0.005846745],"genre_scores_gemma":[0.9984663,0.00009132604,0.001270633,0.000007270679,0.000006708049,0.000003651143,0.000008170319,0.000007664947,0.0001384299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007315787,"threshold_uncertainty_score":0.002880812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516349788635585,"score_gpt":0.1927798037160372,"score_spread":0.1476163058296814,"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."}}