{"id":"W2945374275","doi":"10.6001/energetika.v65i1.3971","title":"DFIG wind turbine under unbalanced power system conditions using adaptive fuzzy virtual inertia controller","year":2019,"lang":"en","type":"article","venue":"Energetika","topic":"Wind Turbine Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Control theory (sociology); Wind power; Induction generator; Fuzzy logic; Maximum power point tracking; Turbine; Controller (irrigation); Automatic frequency control; Fuzzy control system; Electric power system; Computer science; Adaptive neuro fuzzy inference system; Inertia; Control engineering; Power (physics); Engineering; Control (management); Inverter; Voltage","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":[0.0001921738,0.0004634276,0.0002754616,0.0001863094,0.0002789249,0.00034605,0.0003329904,0.0003218668,0.0005221358],"category_scores_gemma":[0.0003546244,0.0001075957,0.0001705274,0.0001340477,0.0001995787,0.0002798071,0.0002057442,0.0002056018,0.00007059766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002114944,"about_ca_system_score_gemma":0.0001708286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002884313,"about_ca_topic_score_gemma":0.003466489,"domain_scores_codex":[0.9999211,0.00001677707,0.000006042008,0.0000179684,0.00002575332,0.00001238082],"domain_scores_gemma":[0.9998729,0.00003295398,0.00003148112,0.00001156393,0.0000408419,0.00001019533],"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":[0.0005005357,0.0001239548,0.00383101,0.0002056784,0.00005953842,0.0006349845,0.0001508182,0.8628426,0.05161981,0.002446818,0.001231727,0.07635237],"study_design_scores_gemma":[0.00003454518,0.0001483263,0.001569222,0.000005074136,0.00001716561,0.00004329962,0.00001807725,0.9948259,0.002548756,0.0004504555,0.000332828,0.000006265052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.631418,0.0002909204,0.3562527,0.0001635136,0.0001175224,0.0001258047,0.00008306403,0.0008054422,0.01074304],"genre_scores_gemma":[0.9969785,0.00002272484,0.002673327,0.000005572829,0.000002809853,0.00001116326,0.00001449977,0.000002531958,0.0002889638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002884313,"threshold_uncertainty_score":0.005735099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006885677663856804,"score_gpt":0.1940036363883433,"score_spread":0.1871179587244864,"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."}}