{"id":"W4403416638","doi":"10.1016/j.epsr.2024.111143","title":"Fault classification in distribution system utilizing imaging time-series, convolutional neural network and adaptive relay protection","year":2024,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Relay; Convolutional neural network; Fault (geology); Computer science; Artificial neural network; Series (stratigraphy); Time series; Artificial intelligence; Pattern recognition (psychology); Machine learning; Seismology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003320208,0.0002870928,0.0003365609,0.0007115345,0.0003118005,0.0004584932,0.0001931589,0.0002421725,0.000005512541],"category_scores_gemma":[0.0001200729,0.0002984469,0.00005949731,0.002887624,0.00007056142,0.0007399132,0.000055684,0.001211402,0.0001600844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002990935,"about_ca_system_score_gemma":0.0001399101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004440644,"about_ca_topic_score_gemma":0.00002693482,"domain_scores_codex":[0.9960333,0.0007866584,0.0006675078,0.0006293436,0.0009057733,0.0009774581],"domain_scores_gemma":[0.9990061,0.0002222815,0.00005783756,0.0003095971,0.0002559362,0.000148274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002070954,0.0003239885,0.005555697,0.01534666,0.001508078,0.002184204,0.005234682,0.1936001,0.3932292,0.1121965,0.1546173,0.1141326],"study_design_scores_gemma":[0.0002151332,0.0001135443,0.001900377,0.0008097613,0.000008506065,0.0007066629,0.0004913392,0.9707205,0.0002339235,0.00002656818,0.02450462,0.0002690777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2884218,0.1537369,0.4963945,0.0006129285,0.01933355,0.01278043,0.0002509912,0.008490304,0.01997872],"genre_scores_gemma":[0.9982663,0.00006295425,0.00002000989,0.000001131001,0.000362713,0.0006924938,0.00004891828,0.00007604599,0.0004693881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7771204,"threshold_uncertainty_score":0.9999468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02802590806505584,"score_gpt":0.2739322953019292,"score_spread":0.2459063872368734,"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."}}