{"id":"W3037752876","doi":"10.3390/electronics9061043","title":"Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Wind power; Electric power system; Mathematical optimization; Weibull distribution; Minification; Electricity generation; Heuristic; Power (physics); Engineering; Turbine; Computer science; Control theory (sociology); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001401226,0.0002562767,0.0002436443,0.00007982684,0.0001264806,0.0001310705,0.0001058427,0.0001374532,0.00002295908],"category_scores_gemma":[0.0000444315,0.0002858669,0.00003547074,0.0003844764,0.00001637093,0.0003800909,0.00003305281,0.0002830094,0.00001193892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002269981,"about_ca_system_score_gemma":0.0001037975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003019398,"about_ca_topic_score_gemma":0.00001105268,"domain_scores_codex":[0.9986625,0.00004286732,0.0003192419,0.0003034287,0.0002302809,0.0004416447],"domain_scores_gemma":[0.9995244,0.00003003988,0.00008976412,0.0001365022,0.0000764825,0.0001428044],"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":[0.000004416008,0.000007200359,0.00002950252,0.00001804612,0.00005653019,0.000005105362,0.0005295311,0.9859055,0.01068437,0.00006420539,0.0001423332,0.002553269],"study_design_scores_gemma":[0.0002845452,0.0001675832,0.00002544361,0.0000178208,0.00002887444,0.0000325923,0.00003919541,0.9962308,0.002330267,0.00000892856,0.0005331783,0.0003008111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2126139,0.006328294,0.78009,0.00007009907,0.0002107773,0.0002650423,0.000008265247,0.0002648207,0.0001487888],"genre_scores_gemma":[0.9497529,0.00003612674,0.04979308,0.0001107848,0.0001772883,0.000004594693,0.00003886522,0.00007728609,0.00000902289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.737139,"threshold_uncertainty_score":0.9999593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120506185980204,"score_gpt":0.2103611781873465,"score_spread":0.1983105595893261,"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."}}