{"id":"W4242273836","doi":"10.1109/tia.2016.2582827","title":"The Formulation of a Power Flow Using &lt;inline-formula&gt; &lt;tex-math notation=\"LaTeX\"&gt;$d-q$&lt;/tex-math&gt; &lt;/inline-formula&gt; Reference Frame Components—Part I: Balanced &lt;inline-formula&gt; &lt;tex-math notation=\"LaTeX\"&gt;$3\\phi$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Systems","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Jacobian matrix and determinant; AC power; Mathematics; Convergence (economics); Control theory (sociology); Reference frame; Power (physics); Applied mathematics; Voltage; Engineering; Computer science; Frame (networking); Electrical engineering; Physics","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":[0.0005368477,0.0008685783,0.0004247799,0.0004256847,0.0003318799,0.001226984,0.0007896376,0.0008365609,0.0169693],"category_scores_gemma":[0.001054019,0.0003488096,0.0006222913,0.0006501207,0.0004958875,0.001523187,0.0004807532,0.001276127,0.003802317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005234715,"about_ca_system_score_gemma":0.001002116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003019943,"about_ca_topic_score_gemma":0.003001678,"domain_scores_codex":[0.9997627,0.00005867366,0.00001686347,0.00005110252,0.00009739178,0.00001331274],"domain_scores_gemma":[0.9997508,0.0001034231,0.00002413414,0.00002913849,0.0000864727,0.000005923956],"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":[0.0000299948,0.00006700872,0.0002902817,0.0004379493,0.0000260072,0.000240551,0.0001956032,0.5356243,0.01219189,0.2811352,0.01290356,0.1568576],"study_design_scores_gemma":[0.00001617539,0.00004332615,0.0001290445,0.00004561191,0.00001030591,0.0001035997,0.00004117001,0.9154077,0.002910988,0.03954143,0.04173715,0.00001349389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009452409,0.0000731461,0.9911706,0.000129289,0.00009428924,0.00004892781,0.0001421222,0.0001194377,0.007276907],"genre_scores_gemma":[0.0968411,0.00103096,0.8644914,0.0002867412,0.0003883683,0.0004706426,0.0009953385,0.0004847444,0.03501072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0169693,"threshold_uncertainty_score":0.05676794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453700293385918,"score_gpt":0.2560452410467817,"score_spread":0.2315082381129225,"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."}}