{"id":"W4226078321","doi":"10.1109/tpwrs.2022.3165210","title":"A Machine Learning-Based Framework for Fast Prediction of Wide-Area Remedial Control Actions in Interconnected Power Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; SaskPower","keywords":"Transient (computer programming); Islanding; Electric power system; Control theory (sociology); Generator (circuit theory); Fault (geology); Computer science; Stability (learning theory); AC power; Microgrid; Transmission line; Engineering; Control engineering; Voltage; Power (physics); Control (management); Machine learning; Artificial intelligence","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":[0.0008733781,0.0009916207,0.00112208,0.0006075412,0.0004443465,0.0007728096,0.001234783,0.0007580828,0.001330575],"category_scores_gemma":[0.002119588,0.0004098481,0.000488023,0.0006097442,0.0005442467,0.0008176456,0.000723568,0.001448619,0.0002513205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000886395,"about_ca_system_score_gemma":0.001464426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279436,"about_ca_topic_score_gemma":0.009597846,"domain_scores_codex":[0.9995838,0.0001026245,0.00002390313,0.0001147402,0.0001200954,0.00005477057],"domain_scores_gemma":[0.9993506,0.0003494162,0.00009630568,0.00003813087,0.0001338371,0.00003183223],"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":[0.00001753789,0.00002464367,0.0001987217,0.00001659014,0.00001379519,0.00002033907,0.000009433646,0.9733369,0.0003979139,0.001979033,0.0003209836,0.02366398],"study_design_scores_gemma":[0.000001115844,0.000004693928,0.00001691546,9.221514e-7,8.658598e-7,0.000001439348,6.415462e-7,0.9994361,0.00005087798,0.0004332129,0.00005227067,8.349826e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006002153,0.000187576,0.9924521,0.00009479512,0.00002337662,0.00002593137,0.00004649581,0.0005289691,0.0006386365],"genre_scores_gemma":[0.7347084,0.0003113032,0.2627502,0.0001335328,0.0001176517,0.0002602914,0.0002870909,0.00008369645,0.0013478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01279436,"threshold_uncertainty_score":0.0254398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522390337884649,"score_gpt":0.2173050786799521,"score_spread":0.2020811753011056,"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."}}