{"id":"W4200607551","doi":"10.18280/jesa.540615","title":"Optimal Fractional-Order PI Control Design for a Variable Speed PMSG-Based Wind Turbine","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Settling time; Particle swarm optimization; PID controller; Permanent magnet synchronous generator; Wind power; Robustness (evolution); Overshoot (microwave communication); Electronic speed control; Wind speed; Turbine; Variable speed wind turbine; Computer science; Control engineering; Step response; Mathematics; Engineering; Magnet; Mathematical optimization; Temperature control; Physics; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001063731,0.0004900823,0.0008432043,0.0002565626,0.00037507,0.0005564201,0.0003390577,0.0002137528,0.0005838079],"category_scores_gemma":[0.0008706477,0.0004636459,0.0003094189,0.0005847638,0.00005296441,0.0005126979,0.00002633912,0.0005485972,0.0001335447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004805928,"about_ca_system_score_gemma":0.0005138993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001118055,"about_ca_topic_score_gemma":0.000002661672,"domain_scores_codex":[0.9967605,0.0004136384,0.001061167,0.0003644979,0.0006027371,0.0007973916],"domain_scores_gemma":[0.9970559,0.000937132,0.0003183598,0.0004124553,0.000924028,0.0003520845],"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":[0.000104126,0.00009379406,0.0002712017,0.0003240581,0.0006270431,0.0002757806,0.00007611301,0.9618317,0.02024255,0.0001959413,0.01338308,0.002574618],"study_design_scores_gemma":[0.005906994,0.000216825,0.01269638,0.0004202657,0.0002552541,0.001914789,0.00004627755,0.9624572,0.001421274,0.0005842235,0.01352084,0.0005596273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02112454,0.00346276,0.9694622,0.0003800341,0.002120133,0.0008586842,0.00009726716,0.0006742856,0.001820104],"genre_scores_gemma":[0.8719122,0.00002884492,0.1236996,0.0002986724,0.001772908,0.00004406894,0.00003088785,0.0002601455,0.001952611],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8507877,"threshold_uncertainty_score":0.9997815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596100178178491,"score_gpt":0.2296780295473199,"score_spread":0.213717027765535,"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."}}