{"id":"W3003316779","doi":"10.1109/pesgm40551.2019.8974126","title":"Adaptive Distribution Network Topology Reconfiguration via Potential Games","year":2019,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Control reconfiguration; Computer science; Distributed computing; Potential game; Smart grid; Nash equilibrium; Network topology; Grid; Topology (electrical circuits); Cyber-physical system; Electric power system; Power (physics); Mathematical optimization; Computer network; Engineering; Embedded system; Mathematics","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":[0.0006683863,0.0005035489,0.0005521296,0.0004423633,0.000474415,0.0008024739,0.0008689327,0.0006600749,0.001561844],"category_scores_gemma":[0.002689095,0.0002720568,0.0004013919,0.0003545383,0.0009679395,0.001017147,0.000939502,0.0006207195,0.0001369242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152042,"about_ca_system_score_gemma":0.0008901266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004046317,"about_ca_topic_score_gemma":0.003216571,"domain_scores_codex":[0.9996387,0.0001740107,0.00001114033,0.00005570792,0.00006334171,0.00005707459],"domain_scores_gemma":[0.9991131,0.00061263,0.00009568332,0.00004238272,0.00006857811,0.0000675393],"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":[0.00001720625,0.00001342442,0.0001771993,0.00001139815,0.000006899873,0.00003810172,0.00002644931,0.9813749,0.000481831,0.0120439,0.000197631,0.005611133],"study_design_scores_gemma":[0.000006377885,0.00001018636,0.00003039891,0.000001570895,0.000001551729,0.00001000609,0.000007267115,0.9951834,0.00007738522,0.004493362,0.0001769009,0.000001557956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07336859,0.0001268342,0.9163568,0.000286313,0.00002802929,0.0001251069,0.00004385889,0.0001855342,0.00947897],"genre_scores_gemma":[0.9477273,0.00009047829,0.04991555,0.00004399001,0.000008890603,0.0001357859,0.00003768266,0.00002221613,0.002018048],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004046317,"threshold_uncertainty_score":0.008358657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00380202754329654,"score_gpt":0.1823852751735725,"score_spread":0.1785832476302759,"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."}}