{"id":"W4388623134","doi":"10.1109/tia.2023.3332584","title":"Soft Open Point-Based Service Restoration Coordinated With Distributed Generation in Distribution Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dispatchable generation; Distributed generation; Grid; Computer science; Fault (geology); Reliability engineering; Distributed computing; Service (business); Node (physics); Engineering; Single point of failure; Linear programming; Power (physics); Electrical engineering","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":[0.0004389104,0.0004679251,0.0006281536,0.0003029477,0.0004162361,0.0005220282,0.0005998045,0.0003180132,0.0008453646],"category_scores_gemma":[0.0005441225,0.0001872669,0.0002947396,0.0003913601,0.0004322945,0.0005804042,0.0004835581,0.0005045648,0.0001384859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007380483,"about_ca_system_score_gemma":0.0006123579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004321884,"about_ca_topic_score_gemma":0.004462288,"domain_scores_codex":[0.999686,0.0001116051,0.000009997119,0.00005494716,0.00009190839,0.00004557399],"domain_scores_gemma":[0.9997917,0.00006547001,0.00005092274,0.00002123624,0.00004879318,0.00002197434],"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":[0.000228633,0.00007921732,0.0004029254,0.00006432318,0.00001876147,0.0001142151,0.00008103963,0.9108092,0.005943071,0.006329646,0.001051375,0.07487768],"study_design_scores_gemma":[0.000008671364,0.00003793012,0.00006716575,0.000001618096,0.000003247092,0.000009651903,0.000008961054,0.9975576,0.00072434,0.001351807,0.0002264161,0.00000255305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204269,0.0002914314,0.8737484,0.0001595621,0.00007397675,0.00008069321,0.00004762716,0.0007897516,0.004381749],"genre_scores_gemma":[0.9760916,0.00007393849,0.02266028,0.00001968697,0.00001444664,0.00002305711,0.00002672575,0.00002179612,0.001068499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004321884,"threshold_uncertainty_score":0.00859344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02300280886575273,"score_gpt":0.2536074541473541,"score_spread":0.2306046452816014,"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."}}