{"id":"W2900475457","doi":"10.1109/tpwrs.2018.2881254","title":"Line-Wise Optimal Power Flow Using Successive Linear Optimization Technique","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power system; Mathematical optimization; Power flow; Convergence (economics); Nonlinear system; Linear programming; Power (physics); Line (geometry); Computer science; Power Balance; Control theory (sociology); Flow (mathematics); Power-flow study; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005753543,0.0008065,0.0006026444,0.0004491579,0.0002552896,0.0005940848,0.0005593752,0.0004293251,0.003425819],"category_scores_gemma":[0.0008860894,0.0003875605,0.0008048874,0.0006954959,0.0003831851,0.0006832725,0.000493299,0.0008478743,0.000490354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003538184,"about_ca_system_score_gemma":0.0009588571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003162387,"about_ca_topic_score_gemma":0.003137224,"domain_scores_codex":[0.9997131,0.00009924062,0.00001540699,0.00003144776,0.0001219653,0.00001888157],"domain_scores_gemma":[0.9997314,0.0001474292,0.00003493109,0.00001914184,0.00005897824,0.000008099247],"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":[0.00002801624,0.00003047664,0.0002176386,0.0001208268,0.00003264167,0.00006471462,0.00007557491,0.9034996,0.007624694,0.01195987,0.00113676,0.07520915],"study_design_scores_gemma":[0.000005078326,0.0000255786,0.00002177691,0.000002705317,0.000003323984,0.00001092329,0.000003438829,0.9973789,0.0006670866,0.001241363,0.0006375189,0.000002291999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003801574,0.00005577333,0.9941194,0.00003807559,0.00001577239,0.00003581758,0.00001429834,0.000170008,0.001749244],"genre_scores_gemma":[0.2816415,0.0002985331,0.7130446,0.00006695367,0.00005996917,0.0003301829,0.0001473784,0.0001687806,0.004242125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003425819,"threshold_uncertainty_score":0.01146048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127723820355643,"score_gpt":0.2434492677871696,"score_spread":0.2306768857516053,"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."}}