{"id":"W4244633599","doi":"10.32920/ryerson.14655642.v1","title":"Adaptive Power Line Harmonic Detection for Active Filter Applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Harmonic; Power (physics); Line (geometry); Adaptive filter; Electronic engineering; Computer science; Control theory (sociology); Cover (algebra); Noise (video); Adaptive control; Electric power system; Engineering; Mathematics; Control (management); Acoustics; Artificial intelligence; Physics","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":[0.0002649518,0.000427903,0.0002646458,0.0002842265,0.000163024,0.0005582553,0.0005903643,0.0004293773,0.002653919],"category_scores_gemma":[0.001011974,0.0001831843,0.0001523919,0.0002873862,0.0002938476,0.0006358266,0.0002360435,0.0005258943,0.0007552675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002807188,"about_ca_system_score_gemma":0.0002123125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003981493,"about_ca_topic_score_gemma":0.0005494109,"domain_scores_codex":[0.9996569,0.00006250026,0.00001451756,0.00006992866,0.000180393,0.00001569477],"domain_scores_gemma":[0.9995205,0.0002344667,0.00006172612,0.0000676932,0.0001070627,0.000008521036],"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":[0.0001948762,0.000103338,0.00085713,0.0003247051,0.00002797246,0.0001246109,0.0001655231,0.06718368,0.331847,0.02407156,0.001582329,0.5735173],"study_design_scores_gemma":[0.00003315824,0.0002540463,0.0008826051,0.00003212063,0.00001886304,0.0002519083,0.00004184848,0.7948972,0.1779691,0.008051139,0.01754366,0.00002440302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006721857,0.0001756477,0.9912831,0.00003488325,0.00001981643,0.0000332674,0.00001154399,0.0002765055,0.001443324],"genre_scores_gemma":[0.4984073,0.000625519,0.49399,0.00007829972,0.00007772655,0.00009740896,0.00006246701,0.00007888125,0.006582371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002653919,"threshold_uncertainty_score":0.008878291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760684041986787,"score_gpt":0.2736372086667817,"score_spread":0.2260303682469138,"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."}}