{"id":"W2023386221","doi":"10.1016/j.epsr.2009.07.009","title":"Optimal tracking secondary voltage control for the DFIG wind turbines and compensator devices","year":2009,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"AC power; Control theory (sociology); Controller (irrigation); Wind power; Voltage; Voltage optimisation; Engineering; Voltage regulation; Induction generator; Compensation (psychology); Voltage controller; Electric power system; Static VAR compensator; Power (physics); Computer science; Voltage regulator; Voltage droop; Control (management); Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002673098,0.0003128565,0.0005518307,0.0003948358,0.0004430633,0.0005481101,0.0005466685,0.0001885402,0.00001751263],"category_scores_gemma":[0.000258378,0.0002273592,0.0001114135,0.0006679561,0.00005571678,0.0002834754,0.00002541093,0.0007798299,0.00003243591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001619758,"about_ca_system_score_gemma":0.0001132654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008874539,"about_ca_topic_score_gemma":0.00001560532,"domain_scores_codex":[0.9969731,0.0002350882,0.0005408775,0.0004130169,0.0007035236,0.001134377],"domain_scores_gemma":[0.9970538,0.001825517,0.00007013596,0.0004753055,0.0003742825,0.0002009677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002053527,0.0005750905,0.01056239,0.004741258,0.00488839,0.0005107539,0.00764279,0.09487643,0.5042435,0.01420164,0.2625301,0.0931742],"study_design_scores_gemma":[0.006537626,0.001301023,0.05427044,0.0003275971,0.00009326702,0.0003688557,0.0007819661,0.7415518,0.002006382,0.00009094689,0.191656,0.001014074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7194962,0.2141835,0.04179194,0.001512179,0.00295734,0.01050427,0.0001361617,0.001006274,0.008412114],"genre_scores_gemma":[0.9983429,0.00004611755,0.00001838585,0.00005422534,0.0006615741,0.0001650268,0.000004140341,0.00006443774,0.000643233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6466754,"threshold_uncertainty_score":0.9271441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02218917719859485,"score_gpt":0.2807452463076502,"score_spread":0.2585560691090553,"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."}}