{"id":"W2787116879","doi":"10.1109/epec.2017.8286193","title":"Modal identification of power system oscillation using parametric DFT technique","year":2017,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Discrete Fourier transform (general); Parametric statistics; Oscillation (cell signaling); SIGNAL (programming language); Control theory (sociology); Fourier transform; Amplitude; Electric power system; Noise (video); Parametric model; White noise; Damping ratio; System identification; Signal processing; Modal; Power (physics); Acoustics; Mathematics; Computer science; Engineering; Fourier analysis; Physics; Electronic engineering; Mathematical analysis; Optics; Short-time Fourier transform; Vibration; Telecommunications; Digital signal processing; Materials science; Data modeling; Artificial intelligence","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":[0.0003732463,0.00007431245,0.0001363671,0.0001250502,0.00009970269,0.00006842198,0.0001453158,0.00007750637,0.00002459548],"category_scores_gemma":[0.00008176627,0.00007220641,0.00004055021,0.0001192223,0.00002366872,0.0002337651,0.00001899824,0.00003895209,0.000008059113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001090522,"about_ca_system_score_gemma":0.00001109369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004020339,"about_ca_topic_score_gemma":0.00000301248,"domain_scores_codex":[0.9992942,0.00002283268,0.0003492696,0.0001109613,0.0001344013,0.00008839335],"domain_scores_gemma":[0.9991828,0.00001545358,0.0001365116,0.0005310767,0.000103062,0.0000310707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001850794,0.00007749052,0.05377932,0.001438333,0.00009625474,0.000002291494,0.000440574,0.7520041,0.1712374,0.01989955,0.0001978487,0.0008083753],"study_design_scores_gemma":[0.0001055785,0.000005980971,0.02228049,0.00003408607,0.000008335741,0.000004889258,0.00007572454,0.9494008,0.02788432,0.00002880276,0.00006067581,0.000110346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589976,0.00002043816,0.8253497,0.000004742006,0.0003618925,0.000283681,0.000007209957,0.0002084287,0.01476634],"genre_scores_gemma":[0.996034,0.00000154589,0.003872422,6.83254e-7,0.000006593053,0.00001076087,0.000002216311,0.00001163899,0.00006009698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8370365,"threshold_uncertainty_score":0.2944492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834505331495249,"score_gpt":0.2534482407484874,"score_spread":0.2351031874335349,"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."}}