{"id":"W3004073470","doi":"10.1109/pesgm40551.2019.8973741","title":"Asset Condition Anomaly Detections by Using Power Quality Data Analytics","year":2019,"lang":"en","type":"article","venue":"","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Ferroresonance in electricity networks; Troubleshooting; Computer science; Reliability engineering; Circuit breaker; Data quality; Anomaly detection; Electric power system; Power quality; Transformer; Electrical engineering; Power (physics); Engineering; Data mining; Voltage; Operations management","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":[0.0007000443,0.0005457113,0.0002965907,0.001389357,0.0001864944,0.001315444,0.0004647803,0.0003942476,0.0007837643],"category_scores_gemma":[0.002797682,0.0001345381,0.0001410943,0.001013821,0.0002320988,0.001399229,0.0007297506,0.0006421378,0.0004842786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002177519,"about_ca_system_score_gemma":0.0002379801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001427577,"about_ca_topic_score_gemma":0.001759817,"domain_scores_codex":[0.9992238,0.0001534296,0.0000498592,0.0001188179,0.0003993606,0.00005457272],"domain_scores_gemma":[0.9982682,0.0007325424,0.0002137678,0.00027061,0.0004434676,0.0000713283],"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":[0.0005930163,0.0006066232,0.07814875,0.0002695065,0.0001056832,0.001092268,0.001546112,0.04167521,0.1064175,0.004859409,0.008029569,0.7566563],"study_design_scores_gemma":[0.0000515189,0.0004998731,0.03566374,0.00009378259,0.00006848481,0.001239359,0.001312004,0.781132,0.1485268,0.01038584,0.02093326,0.00009318678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5160311,0.0005016505,0.4564222,0.001060281,0.00009445094,0.0002569049,0.001243953,0.008731936,0.01565748],"genre_scores_gemma":[0.928674,0.0001741268,0.06958298,0.00006466787,0.00002574185,0.00002206263,0.0004800498,0.00008552228,0.0008907263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001427577,"threshold_uncertainty_score":0.003702223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04363742901651466,"score_gpt":0.2998238823806169,"score_spread":0.2561864533641023,"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."}}