{"id":"W3127329121","doi":"10.1109/epec48502.2020.9320127","title":"A Reinforcement Learning based Power System Stabilizer for a Grid Connected Wind Energy Conversion System","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Wind power; Control theory (sociology); Controller (irrigation); Wind speed; Pitch control; Electric power system; Rotor (electric); Induction generator; Computer science; Reinforcement learning; Grid; AC power; Engineering; Power (physics); Voltage; Control (management); Electrical engineering; Physics","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":[0.0004708201,0.0004571019,0.0004853836,0.0001859085,0.0003571545,0.0004418803,0.0005706406,0.0005251147,0.001968445],"category_scores_gemma":[0.0006982327,0.000169316,0.0002629534,0.0001187026,0.0003820298,0.0002506992,0.0003319796,0.0005920576,0.0003151282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004137488,"about_ca_system_score_gemma":0.0004396648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0036049,"about_ca_topic_score_gemma":0.00254342,"domain_scores_codex":[0.9997784,0.00006064586,0.00001394859,0.00005504308,0.00006878371,0.00002319378],"domain_scores_gemma":[0.9997719,0.00007127351,0.00004767304,0.00001761212,0.00007603911,0.00001546413],"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":[0.0003518949,0.0001831592,0.0009825445,0.0001735503,0.00008854635,0.0004610276,0.0001323261,0.8723895,0.03391644,0.004605836,0.001563689,0.08515144],"study_design_scores_gemma":[0.00003572589,0.0001536545,0.0001807599,0.00000554682,0.00001044692,0.00003259024,0.000003634572,0.997177,0.001699119,0.0002662691,0.0004292896,0.000005886466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.124819,0.000444831,0.8634418,0.0004593534,0.0001786987,0.0001725541,0.00004777381,0.002225232,0.008210721],"genre_scores_gemma":[0.9816745,0.00006291109,0.01646217,0.00004941085,0.00001504777,0.00005543218,0.00001757211,0.00001327681,0.001649596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0036049,"threshold_uncertainty_score":0.007167816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005824301042855147,"score_gpt":0.1633041774915436,"score_spread":0.1574798764486885,"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."}}