{"id":"W3111415822","doi":"10.1109/access.2020.3043935","title":"Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"RTDS Technologies (Canada); University of Manitoba","funders":"Mitacs","keywords":"Artificial intelligence; Machine learning; Robustness (evolution); Computer science; Stability (learning theory); Margin (machine learning); Random forest; Support vector machine; Feature selection; Voltage; Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005765343,0.0006913728,0.0006293874,0.00114563,0.0001973234,0.0006041327,0.000519951,0.0004390855,0.0007788052],"category_scores_gemma":[0.002274198,0.0001623688,0.0002816906,0.0009023667,0.0001810597,0.0008612641,0.0002602703,0.0005893248,0.000434776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003392322,"about_ca_system_score_gemma":0.0002288006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001237098,"about_ca_topic_score_gemma":0.001567595,"domain_scores_codex":[0.9995321,0.00009619447,0.00003414619,0.0001101822,0.000202715,0.0000246452],"domain_scores_gemma":[0.9992697,0.0003116168,0.0001662709,0.0000686831,0.0001655794,0.00001818903],"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":[0.0002106237,0.0002355969,0.01442874,0.0001652099,0.0001468269,0.0001083892,0.000115163,0.4416279,0.03272303,0.002749295,0.001827941,0.5056613],"study_design_scores_gemma":[0.000004034321,0.00005092713,0.003256595,0.000009646697,0.000009795911,0.00003612807,0.000007437638,0.9892483,0.0061795,0.0006774947,0.0005074281,0.00001281448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07802495,0.0003100148,0.9166286,0.00009767541,0.0000447479,0.00004807649,0.0002420131,0.002150343,0.002453544],"genre_scores_gemma":[0.850206,0.0002657138,0.1476492,0.00003821667,0.00004817573,0.0000877226,0.0004067802,0.00006347217,0.001234816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001237098,"threshold_uncertainty_score":0.003049076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05322557638951686,"score_gpt":0.2867156015552189,"score_spread":0.233490025165702,"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."}}