{"id":"W4413997858","doi":"10.18280/jesa.580720","title":"Optimization of DFIG Performance in Wind Energy Systems Using Fuzzy Logic Control and Harmonic Mitigation","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Doubly fed electric machine; Fuzzy logic; Wind power; Control theory (sociology); Pitch control; Harmonic; Control (management); Computer science; Energy (signal processing); Control engineering; Engineering; Mathematics; AC power; Physics; Electrical engineering; Artificial intelligence; Voltage; Acoustics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004777451,0.0002181133,0.000505899,0.0004921871,0.0001041295,0.0001499144,0.0001522704,0.0001158564,0.00000488869],"category_scores_gemma":[0.00006875254,0.0002040276,0.00006081314,0.0004403338,0.00005302309,0.0004318922,0.00002260154,0.0001949837,0.000001478032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003229434,"about_ca_system_score_gemma":0.00007094584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000557737,"about_ca_topic_score_gemma":0.000004635868,"domain_scores_codex":[0.9982123,0.0002189556,0.0009000296,0.0001529843,0.000228449,0.0002872879],"domain_scores_gemma":[0.9992443,0.00009016015,0.0002792071,0.0001599175,0.0001527427,0.00007363853],"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":[0.00001397483,0.00001412002,0.01228563,0.0006876821,0.0001033184,0.00001631612,0.0001075707,0.9762357,0.004262041,0.0004573583,0.00003849731,0.005777729],"study_design_scores_gemma":[0.001238472,0.00004367132,0.1253295,0.001288044,0.00005059999,0.0002412577,0.00005029926,0.8712882,0.0001415736,0.0001467201,0.00003778864,0.0001438847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910632,0.01244143,0.09319104,0.00003324592,0.0009642348,0.0003402687,0.000009363992,0.0001577195,0.001799536],"genre_scores_gemma":[0.998393,0.0003478884,0.0009995317,0.00001609801,0.0001143269,0.000007529836,0.000002394052,0.0000348388,0.00008440141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1130439,"threshold_uncertainty_score":0.8320003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014450275188999,"score_gpt":0.2127406850192571,"score_spread":0.2025961822673671,"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."}}