{"id":"W2057615432","doi":"10.1109/pes.2010.5590093","title":"Online monitoring of voltage stability margin using an Artificial Neural Network","year":2010,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Phasor; Margin (machine learning); Voltage; Artificial neural network; Control theory (sociology); Generator (circuit theory); Electric power system; Stability (learning theory); Units of measurement; Computer science; Engineering; Power (physics); Artificial intelligence; Control (management); Electrical engineering; Machine learning","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":[0.000256552,0.0002997823,0.0002861073,0.0004321245,0.000157419,0.0003323976,0.0002720449,0.0003752047,0.0005652552],"category_scores_gemma":[0.001175625,0.000127416,0.00008511828,0.0003222475,0.0001357799,0.0004445653,0.000181802,0.0003003706,0.000135372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604518,"about_ca_system_score_gemma":0.0001403941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158482,"about_ca_topic_score_gemma":0.002098171,"domain_scores_codex":[0.999783,0.00005173358,0.00001289498,0.00004458901,0.00009570421,0.00001209704],"domain_scores_gemma":[0.9996278,0.0001617286,0.00007385596,0.00002492288,0.0001002892,0.00001122486],"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":[0.0006088244,0.000202163,0.01646162,0.0001811111,0.00008024588,0.000222022,0.0001161134,0.3526523,0.08330046,0.001658877,0.001209833,0.5433065],"study_design_scores_gemma":[0.00000494165,0.00005203626,0.002505044,0.000005721623,0.000005528784,0.00003274873,0.000004683,0.9894007,0.007424613,0.0002454279,0.0003108805,0.000007575553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.313685,0.0005573679,0.6800312,0.0001762409,0.00008603665,0.00003555439,0.0001408886,0.001032011,0.004255581],"genre_scores_gemma":[0.9576498,0.0001088298,0.04128784,0.00002673058,0.00002003712,0.0000220011,0.00005780719,0.00001133373,0.0008156223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001158482,"threshold_uncertainty_score":0.002303481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04327739832431203,"score_gpt":0.2687515775101038,"score_spread":0.2254741791857918,"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."}}