{"id":"W2655606959","doi":"10.1109/ccece.2017.7946717","title":"Data fusion for fault diagnosis in smart grid power systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Fault (geology); Smart grid; Electric power system; Computer science; Sensor fusion; Wavelet transform; Circuit breaker; Artificial neural network; Reliability (semiconductor); Wavelet; Operator (biology); Real-time computing; Fault detection and isolation; Grid; Data mining; Power (physics); Reliability engineering; Engineering; Artificial intelligence","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":[0.0009522702,0.0004982264,0.0006818345,0.0008506492,0.0004030804,0.0006719259,0.0004081457,0.0005141072,0.0006909391],"category_scores_gemma":[0.001818652,0.0001751076,0.0003973337,0.0008843657,0.0003507974,0.001249152,0.0006029897,0.0006243941,0.0001827557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005906431,"about_ca_system_score_gemma":0.0004598592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002800493,"about_ca_topic_score_gemma":0.001531268,"domain_scores_codex":[0.9994696,0.0001373373,0.00004931936,0.00010044,0.0002113636,0.00003194712],"domain_scores_gemma":[0.9995764,0.0001827294,0.00005177722,0.00005513523,0.0001200442,0.00001392153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003935357,0.00009811304,0.001889838,0.0002415112,0.00009809442,0.0001786616,0.0001890674,0.4477088,0.01816255,0.01372012,0.002060548,0.5152593],"study_design_scores_gemma":[0.000008712273,0.00003800548,0.0004152235,0.000007413088,0.00001029419,0.00002741377,0.00002118241,0.9884591,0.004418238,0.005625547,0.0009598758,0.000009020243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02574463,0.001000048,0.9711652,0.0001952356,0.00008262948,0.00004417804,0.00009490163,0.0006602486,0.001012793],"genre_scores_gemma":[0.8489197,0.000747711,0.1492682,0.00008283865,0.00006143026,0.00006610673,0.0002354369,0.00002930284,0.0005892521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002800493,"threshold_uncertainty_score":0.005568326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04006543078688037,"score_gpt":0.2865204877099785,"score_spread":0.2464550569230981,"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."}}