{"id":"W2560789657","doi":"10.1109/pmaps.2016.7764136","title":"Cumulant-based probabilistic load flow analysis of wind power and electric vehicles","year":2016,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Probabilistic logic; Monte Carlo method; Reliability (semiconductor); Wind power; Computer science; Probabilistic analysis of algorithms; Power-flow study; Cumulant; Random variable; Power (physics); Grid; Electric power system; Mathematical optimization; Reliability engineering; Probability distribution; AC power; Voltage; Engineering; Mathematics; Electrical engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007858629,0.0005560777,0.0003731482,0.001276549,0.0002863278,0.0005882151,0.0004727666,0.0004012782,0.001055898],"category_scores_gemma":[0.00290567,0.0002019043,0.0004734649,0.000781515,0.000384446,0.0009635813,0.0002865269,0.0003768925,0.0001474218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009091019,"about_ca_system_score_gemma":0.0007974485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074984,"about_ca_topic_score_gemma":0.008349566,"domain_scores_codex":[0.9996997,0.00009813158,0.00001004134,0.00003166903,0.0001256979,0.00003477732],"domain_scores_gemma":[0.9991227,0.0005415363,0.0001093193,0.00003988024,0.0001669436,0.00001955626],"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":[0.00001206606,0.00001129716,0.000385799,0.00001550948,0.00001018074,0.00003290149,0.00001244193,0.971498,0.0007655513,0.01760051,0.0003431225,0.00931262],"study_design_scores_gemma":[3.295907e-7,0.000001177863,0.00009538734,6.212397e-7,7.153822e-7,0.000002666531,7.068023e-7,0.99844,0.00006710185,0.00133486,0.00005535869,0.000001138122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05963026,0.0003139414,0.9349809,0.000105829,0.00003283177,0.00004548539,0.0001696823,0.0002264565,0.004494668],"genre_scores_gemma":[0.9379576,0.0004184383,0.05813026,0.00004154547,0.00006207655,0.00007595862,0.0003493546,0.00009749996,0.002867388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01074984,"threshold_uncertainty_score":0.02137452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004164036681440428,"score_gpt":0.1888549480154603,"score_spread":0.1846909113340198,"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."}}