{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001268395,0.00008332272,0.00009615762,0.0000491197,0.00003490171,0.00004259786,0.0001119227,0.00005909658,0.0005170521],"category_scores_gemma":[0.00001656264,0.00008659881,0.00002220374,0.0001307334,0.000009301139,0.0004021115,0.0000159283,0.0000786948,0.00009400165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583587,"about_ca_system_score_gemma":0.00001095614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058605,"about_ca_topic_score_gemma":0.00004702446,"domain_scores_codex":[0.9994203,0.00001035262,0.000187629,0.000138836,0.0001075735,0.0001353339],"domain_scores_gemma":[0.9994951,0.00005183635,0.00001802742,0.0003637487,0.00002876386,0.00004254708],"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":[0.00002485532,0.0003735003,0.1559299,0.000308863,0.0008134443,0.000003892534,0.0003417397,0.1678733,0.6008582,0.008229702,0.06113219,0.004110385],"study_design_scores_gemma":[0.0004001097,0.00003509505,0.04884141,0.00001606161,0.00004975524,0.000002971932,0.00006681425,0.920694,0.007636939,0.0001735916,0.02175942,0.0003238273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8058352,0.00005249516,0.1793609,0.00002261551,0.0003891785,0.0001163835,0.0005309986,0.0001434098,0.0135488],"genre_scores_gemma":[0.9985253,0.00002330959,0.0006732997,0.00004296039,0.00001509594,9.716825e-7,0.0006264472,0.00001530488,0.00007736756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7528207,"threshold_uncertainty_score":0.5661358,"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."}}