{"id":"W2952405286","doi":"10.48550/arxiv.1906.08311","title":"The Effect of the Uncertainty of Load and Renewable Generation on the Dynamic Voltage Stability Margin","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Renewable energy; Margin (machine learning); Voltage; Control theory (sociology); Monte Carlo method; Dynamic load testing; Computer science; Stochastic differential equation; Stability (learning theory); Dynamic demand; Electric power system; Mathematical optimization; Power (physics); Mathematics; Engineering; Applied mathematics; Electrical engineering; Physics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009610806,0.0001911165,0.0002676165,0.00002837894,0.0001182835,0.00002359525,0.0004412306,0.0001480499,0.00002369923],"category_scores_gemma":[0.0001407243,0.0001083547,0.0001424542,0.0001909347,0.0001758964,0.00004012873,0.0002480007,0.0002715066,0.000002161584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002834445,"about_ca_system_score_gemma":0.00006992764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002725271,"about_ca_topic_score_gemma":0.0007467786,"domain_scores_codex":[0.9988568,0.0003812464,0.0002165167,0.0002975465,0.000107074,0.0001407766],"domain_scores_gemma":[0.9980997,0.0004991376,0.0001565565,0.001115342,0.0000995704,0.00002965993],"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":[0.00004877903,0.000008764408,0.006488722,0.0002493892,0.00005685072,3.171623e-7,0.0001188,0.9914408,0.0005440759,0.000911919,0.00009338273,0.00003820167],"study_design_scores_gemma":[0.0002650793,0.00004566149,0.002685774,0.00006545379,0.00005179088,2.070523e-7,0.00008698076,0.9931494,0.003051225,0.0003022195,0.0001797064,0.0001165283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734939,0.0001201986,0.02242476,0.00004065651,0.0005619749,0.0008429847,0.00006760428,0.00003734095,0.002410538],"genre_scores_gemma":[0.9994197,0.00011717,0.000003804351,0.00000454974,0.000006912449,0.000001503184,0.000007034001,0.00001215692,0.0004271118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02592581,"threshold_uncertainty_score":0.4418576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02374337805100626,"score_gpt":0.1605918579504385,"score_spread":0.1368484798994322,"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."}}