{"id":"W4401632785","doi":"10.22215/etd/2024-16094","title":"Learning Based Resilient Control and Vulnerability Management for Wide Area Damping Control with Cyber and Physical Structures","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Reinforcement learning; Phasor; Cyber-physical system; Electric power system; Computer science; Robustness (evolution); Controller (irrigation); Control theory (sociology); Engineering; Control engineering; Power (physics); Artificial intelligence; Control (management)","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":[0.0006791959,0.001021469,0.0006901819,0.0003214212,0.0004121261,0.0009076561,0.0009370119,0.0007460876,0.002092057],"category_scores_gemma":[0.001353279,0.0003798203,0.0006179567,0.000255743,0.0008589075,0.0007566997,0.001349076,0.001463987,0.0002561561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006997372,"about_ca_system_score_gemma":0.0008652383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003919836,"about_ca_topic_score_gemma":0.003063241,"domain_scores_codex":[0.9996835,0.00005490113,0.00001864229,0.0001067404,0.00006885251,0.00006741655],"domain_scores_gemma":[0.9995143,0.0002299833,0.00009721459,0.00003962242,0.00008758384,0.00003125794],"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":[0.0000709375,0.00004900042,0.0003853373,0.00009995294,0.00005233248,0.00007717852,0.00007861559,0.9355344,0.00473808,0.01019654,0.001042874,0.04767471],"study_design_scores_gemma":[0.000005054176,0.00003365807,0.0000665827,0.000004923878,0.000005328,0.00000705272,0.000003736523,0.9976317,0.0003471277,0.001543162,0.0003485167,0.000003206648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01942108,0.0006316855,0.974996,0.0002907548,0.0000858643,0.00003843045,0.00003065164,0.0004499006,0.004055575],"genre_scores_gemma":[0.966031,0.0003547026,0.03022594,0.0001461268,0.00006716869,0.00009849092,0.00005576345,0.00003880995,0.002982036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003919836,"threshold_uncertainty_score":0.007794023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004428075438016306,"score_gpt":0.2240221093960677,"score_spread":0.2195940339580514,"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."}}