{"id":"W3174557638","doi":"10.3390/en14123623","title":"Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Deep learning; Convolutional neural network; Pattern recognition (psychology); Fault detection and isolation; Unsupervised learning","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":[0.0006485326,0.0007801474,0.0004469987,0.0004220236,0.0001563939,0.0004568014,0.0007612496,0.0007154717,0.001056276],"category_scores_gemma":[0.001815812,0.0003565518,0.0003976653,0.000450762,0.0003571735,0.0007482839,0.0004907164,0.001324388,0.0003512033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006979317,"about_ca_system_score_gemma":0.0005981341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009550545,"about_ca_topic_score_gemma":0.009773639,"domain_scores_codex":[0.9998029,0.00003929741,0.00001102417,0.00005356449,0.00005986075,0.00003347246],"domain_scores_gemma":[0.9994222,0.0003078994,0.00006277767,0.00005690952,0.0001310348,0.00001922574],"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":[0.00009078338,0.0001006074,0.001656787,0.00007164776,0.00007911179,0.00007316757,0.00003488468,0.777615,0.007627272,0.003900166,0.002148865,0.2066017],"study_design_scores_gemma":[0.000001285935,0.000007093059,0.0001863661,0.000003112149,0.000003241609,0.000005618514,0.000001744501,0.998024,0.0008660275,0.0007260736,0.0001733284,0.000002116965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03918341,0.0009797817,0.9564079,0.0002535143,0.00005504394,0.00002349316,0.0001380233,0.001114354,0.001844446],"genre_scores_gemma":[0.8330423,0.0009678473,0.1597176,0.0002425178,0.00007546593,0.00006124737,0.0006342623,0.00008919348,0.005169668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009550545,"threshold_uncertainty_score":0.01898992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006244782956768538,"score_gpt":0.2072585600625333,"score_spread":0.2010137771057648,"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."}}