{"id":"W2164880388","doi":"10.1109/innovations.2011.5893877","title":"A multilayer control framework for distribution systems with high DG penetration","year":2011,"lang":"en","type":"article","venue":"","topic":"Islanding Detection in Power Systems","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Supervisor; Penetration (warfare); Distributed generation; Distributed computing; Tap changer; Computer science; Smart grid; Multi-agent system; Distribution management system; Decentralised system; Capacitor; Grid; Layer (electronics); Electric power system; Shunt (medical); Distributed power generation; Control engineering; Control (management); Electrical engineering; Engineering; Power (physics); Materials science; Nanotechnology; Voltage; Renewable energy; Artificial intelligence","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":[0.0007909566,0.0006552922,0.0005175632,0.000322316,0.0004595912,0.001348222,0.0008665324,0.0006208151,0.002430222],"category_scores_gemma":[0.0007416634,0.0001626199,0.000617823,0.0002558607,0.0006598278,0.001295298,0.00119329,0.0009798873,0.0003800928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007964684,"about_ca_system_score_gemma":0.001072123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004837952,"about_ca_topic_score_gemma":0.004130028,"domain_scores_codex":[0.9996092,0.00009822175,0.00002938786,0.00008794705,0.0001223499,0.00005288574],"domain_scores_gemma":[0.9997711,0.0000580262,0.00003994721,0.00003028076,0.00006585396,0.00003478911],"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":[0.00006255588,0.00006226555,0.0004734003,0.0001330161,0.00005007397,0.0001697433,0.0001841023,0.7541883,0.007492898,0.1823372,0.001683401,0.05316306],"study_design_scores_gemma":[0.00001132335,0.00004481481,0.0000971253,0.00001126773,0.00001169149,0.00001837667,0.0000188691,0.9751151,0.0007193222,0.02040466,0.00353906,0.000008420261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002885717,0.0001201774,0.9946187,0.0001025087,0.00002926787,0.0000249013,0.00002182109,0.0001348627,0.002062054],"genre_scores_gemma":[0.7293128,0.0004861327,0.263696,0.0001354105,0.0001137483,0.0002771869,0.0001132935,0.00004575191,0.005819612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004837952,"threshold_uncertainty_score":0.009619594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370099176281674,"score_gpt":0.1951795458141969,"score_spread":0.1814785540513801,"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."}}