{"id":"W2045715296","doi":"10.5539/mas.v4n8p33","title":"Application of Differential Evolution for Congestion Management in Power System","year":2010,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Electric Power System Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Congestion management; Particle swarm optimization; Differential evolution; Computer science; Reliability (semiconductor); Electric power system; Mathematical optimization; Electricity market; Database transaction; Electricity; Differential (mechanical device); Work (physics); Order (exchange); Management system; Operations research; Reliability engineering; Power (physics); Algorithm; Operations management; Mathematics; Business; Economics; Engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005121332,0.0002572663,0.0002947187,0.0002765225,0.0002032703,0.000352465,0.0003664814,0.0003557485,0.0004715945],"category_scores_gemma":[0.001082192,0.0001546362,0.0002237374,0.0002987549,0.0003328939,0.0003038905,0.0004593493,0.0003024554,0.0000457212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175412,"about_ca_system_score_gemma":0.0002390702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002043062,"about_ca_topic_score_gemma":0.001358593,"domain_scores_codex":[0.9998591,0.00006850938,0.000005299614,0.00001533172,0.00003766142,0.00001419105],"domain_scores_gemma":[0.9998308,0.0001042831,0.00001836022,0.000009281904,0.00002760714,0.000009581495],"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":[0.00003255351,0.00002162344,0.0007798349,0.00002955862,0.00002464567,0.00007887945,0.00004999172,0.9518501,0.00288911,0.007852231,0.0002274209,0.03616415],"study_design_scores_gemma":[0.000003031361,0.00001596872,0.00008689793,0.000001092914,0.000001925878,0.000008700809,0.00000245637,0.9985537,0.0002677952,0.0008552851,0.0002017678,0.000001388149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1119721,0.0004962281,0.8811823,0.000315161,0.00005129205,0.00004811513,0.0000165736,0.0001187025,0.005799499],"genre_scores_gemma":[0.9621017,0.0001819511,0.03655813,0.00003341266,0.00001260287,0.00003531791,0.00001326599,0.00001180994,0.001051761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002043062,"threshold_uncertainty_score":0.004062355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003084668661137298,"score_gpt":0.1921479679280912,"score_spread":0.1890632992669539,"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."}}