{"id":"W4253879548","doi":"10.1109/59.932291","title":"Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Phasor; Computer science; Stability (learning theory); Reliability engineering; Fuzzy logic; Electric power system; Contingency; Grid; Blackout; Discrete Fourier transform (general); Time domain; Engineering; Control theory (sociology); Real-time computing; Artificial intelligence; Machine learning; Fourier transform; Mathematics; Control (management); Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005182864,0.0006344776,0.0005925329,0.0008943012,0.0003463137,0.0007199188,0.0004177823,0.0003846703,0.00194958],"category_scores_gemma":[0.001746862,0.0001899991,0.0002934232,0.0005937514,0.0003271761,0.0006301594,0.0003909388,0.0005160989,0.0004017902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004449076,"about_ca_system_score_gemma":0.000517805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493212,"about_ca_topic_score_gemma":0.004365487,"domain_scores_codex":[0.9995963,0.0002061038,0.00002236428,0.00003842828,0.0001132676,0.00002349571],"domain_scores_gemma":[0.9994598,0.0003330925,0.00005408639,0.00004915702,0.00008694908,0.00001690575],"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":[0.0002507891,0.0001546231,0.002618325,0.0001811733,0.00008718886,0.0001845625,0.0001406125,0.3221351,0.0290297,0.02281415,0.001941782,0.620462],"study_design_scores_gemma":[0.00001253674,0.00007588098,0.0008909657,0.00001275756,0.00001526159,0.00004151361,0.00001934635,0.9907012,0.00258763,0.004139442,0.00149112,0.00001243844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01836086,0.000263006,0.9785227,0.00007620763,0.00001947108,0.00007208598,0.00003246617,0.0008179601,0.00183532],"genre_scores_gemma":[0.4169828,0.0003104346,0.5808907,0.00004358805,0.0000340951,0.0002428285,0.0001100789,0.00003845667,0.001347007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002493212,"threshold_uncertainty_score":0.006522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02110018009855245,"score_gpt":0.2828222029844141,"score_spread":0.2617220228858617,"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."}}