{"id":"W3201840695","doi":"10.1109/jsyst.2021.3112710","title":"A Supervised Learning Approach for Centralized Fault Localization in Smart Microgrids","year":2021,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microgrid; Backup; Distributed generation; Photovoltaic system; Computer science; Smart grid; Wind power; Fault detection and isolation; Reliability (semiconductor); Control engineering; Phasor measurement unit; Fault (geology); Distributed computing; Engineering; Voltage; Electric power system; Artificial intelligence; Renewable energy; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003461998,0.0001388704,0.0002615659,0.0001155014,0.0001028534,0.0001984072,0.00008350943,0.0001123914,0.00001303712],"category_scores_gemma":[0.00003464842,0.0001388026,0.00009781532,0.0002397786,0.000007965601,0.0001526948,0.000005197869,0.0002475632,0.000003435763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253701,"about_ca_system_score_gemma":0.0000473521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001197393,"about_ca_topic_score_gemma":0.000006981857,"domain_scores_codex":[0.9988569,0.0001201436,0.0004465921,0.0001396469,0.000135764,0.0003009603],"domain_scores_gemma":[0.999566,0.00002542018,0.00006127492,0.00008611482,0.0001744276,0.00008680011],"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":[0.00001780285,0.00002663529,0.001077848,0.0001668941,0.00004501922,0.00001423995,0.0002720362,0.9834065,0.01256213,0.00001027541,0.0008983948,0.001502215],"study_design_scores_gemma":[0.00201833,0.00001649648,0.00004550083,0.00009227522,0.00001965344,0.0002489774,0.0002238906,0.9845316,0.001325189,0.000005814984,0.01131378,0.0001584887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02715932,0.006907092,0.9636047,0.00002544293,0.001518731,0.0003314345,0.000006975591,0.0001076246,0.0003386174],"genre_scores_gemma":[0.9944607,0.0006598341,0.004017089,0.00002659675,0.0005215408,0.00004344822,0.00007230252,0.00004901436,0.0001494535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9673014,"threshold_uncertainty_score":0.5660208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010510533014432,"score_gpt":0.2049890787577358,"score_spread":0.1944785457433038,"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."}}