{"id":"W4400680548","doi":"10.1109/tia.2024.3429080","title":"Multi-Agent Reinforcement Learning-Based Maximum Power Point Tracking Approach to Fortify PMSG-Based WECSs","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Control theory (sociology); Tracking (education); Computer science; Reinforcement; Point (geometry); Power (physics); Engineering; Artificial intelligence; Physics; Mathematics; Control (management); Structural 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001373355,0.0003776485,0.0002276995,0.0004258027,0.0002962719,0.0001209221,0.0002762315,0.0003705489,0.0002832051],"category_scores_gemma":[0.000004059445,0.0004255765,0.0001972983,0.0009299896,0.00005432879,0.0001979788,0.000001989637,0.001580584,0.0004335917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005469023,"about_ca_system_score_gemma":0.000100581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001218013,"about_ca_topic_score_gemma":0.000005618609,"domain_scores_codex":[0.9981341,0.00002681859,0.0004794309,0.0005589182,0.0003135409,0.000487173],"domain_scores_gemma":[0.9988677,0.0001033424,0.00003845819,0.0006209699,0.00006805479,0.000301495],"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":[0.0000114601,0.0001663136,0.000003994981,0.00009926865,0.00006828616,0.000002661332,0.0001680923,0.952534,0.004190532,0.00005801312,0.0002690773,0.04242836],"study_design_scores_gemma":[0.0004497199,0.00006611219,0.00003613726,0.00009644646,0.00007168594,0.000005707896,0.0002389627,0.9470816,0.02104369,0.00001705459,0.03042718,0.0004657191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006632683,0.00004107469,0.9945253,0.0005087929,0.0004149629,0.001343905,0.00005499895,0.001637304,0.0008104398],"genre_scores_gemma":[0.9829843,0.000004370933,0.01233147,0.0003000809,0.00005254546,0.003498976,0.00003906544,0.0001322539,0.0006569523],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.982321,"threshold_uncertainty_score":0.9998196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02444184252063897,"score_gpt":0.2643703456049076,"score_spread":0.2399285030842686,"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."}}