{"id":"W2774023991","doi":"10.48550/arxiv.1712.01317","title":"State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kalman filter; Snapshot (computer storage); Phasor; Computer science; Estimator; Electric power system; State estimator; Power flow; State (computer science); Mathematical optimization; Control theory (sociology); Algorithm; Power (physics); Mathematics; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002432411,0.0003739939,0.0003490812,0.0002143998,0.0001130089,0.0001593527,0.0004410948,0.0003114137,0.00001735672],"category_scores_gemma":[0.00005726512,0.0004956093,0.0001221096,0.0001692942,0.00004873407,0.0002826999,0.0001756107,0.0006104787,0.0001151551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235123,"about_ca_system_score_gemma":0.00006083892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001751887,"about_ca_topic_score_gemma":0.00003763813,"domain_scores_codex":[0.998588,0.00006678904,0.0002585443,0.0005776289,0.0001095715,0.000399496],"domain_scores_gemma":[0.9986928,0.00004968173,0.0001622305,0.0009008227,0.00007833431,0.0001161417],"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":[0.00005131148,0.00004187199,0.0007288592,0.0002201572,0.00002568079,0.0001469826,0.00002520682,0.9970795,0.0001137769,0.00113375,0.0003240737,0.0001088435],"study_design_scores_gemma":[0.000505074,0.00005052469,0.004915468,0.0005227315,0.00003021798,0.000001151151,0.00001594657,0.9922867,0.0003483292,0.000618923,0.0002556436,0.0004493032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056193,0.00002700134,0.49016,0.000009742512,0.0009059623,0.0003611943,0.00049682,0.0003071618,0.002112755],"genre_scores_gemma":[0.9984982,0.00003141943,0.00007313713,0.000003983806,0.00002053648,0.000004212068,0.00118676,0.00004213056,0.0001396394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4928788,"threshold_uncertainty_score":0.9997495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03353577428022466,"score_gpt":0.1835454372015621,"score_spread":0.1500096629213374,"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."}}