{"id":"W2026634928","doi":"10.1049/ip-gtd:20030943","title":"Discrete-Fourier-transform-based technique for removal of decaying DC offset from phasor estimates","year":2003,"lang":"en","type":"article","venue":"IEE Proceedings - Generation Transmission and Distribution","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Western University","funders":"","keywords":"Phasor; DC bias; Offset (computer science); Fourier transform; Algorithm; Electronic engineering; Relay; Fast Fourier transform; Computer science; Discrete Fourier transform (general); Fault (geology); Control theory (sociology); Fractional Fourier transform; Mathematics; Fourier analysis; Engineering; Physics; Voltage; Electrical engineering; Electric power system; Power (physics); Artificial intelligence; Mathematical analysis","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":[0.0003561346,0.0004863006,0.0004081995,0.0005206759,0.0002633852,0.0004442838,0.0005479222,0.000414166,0.002098463],"category_scores_gemma":[0.002227735,0.0001998974,0.0002064926,0.0005199311,0.0002871748,0.0007527357,0.0003387055,0.0008732895,0.0009566766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631461,"about_ca_system_score_gemma":0.0005071007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006965423,"about_ca_topic_score_gemma":0.001156818,"domain_scores_codex":[0.9997593,0.00002326644,0.00001474003,0.00002778124,0.0001624375,0.00001244821],"domain_scores_gemma":[0.9993271,0.0003073221,0.00006646441,0.00008551186,0.0001940472,0.00001954831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002314619,0.00009260962,0.0004173373,0.0002330574,0.00003179533,0.0001884338,0.0001547344,0.01129141,0.3276768,0.006041792,0.001648254,0.6519924],"study_design_scores_gemma":[0.0001190093,0.0005085169,0.002953012,0.00006840337,0.00008788043,0.002262684,0.00007632243,0.5638946,0.4011666,0.003775418,0.02501947,0.00006795707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009669402,0.0001628571,0.9887983,0.00005868025,0.0000521402,0.00002331509,0.00002556575,0.0003829057,0.0008268051],"genre_scores_gemma":[0.1490534,0.0004451358,0.8473389,0.00007674284,0.00008301576,0.00007639795,0.0001389783,0.00008711143,0.002700401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002098463,"threshold_uncertainty_score":0.007020056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129965874624862,"score_gpt":0.2438917017111647,"score_spread":0.2308951142486785,"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."}}