{"id":"W2940185916","doi":"10.1109/cjece.2019.2891272","title":"Sag and Flicker Reduction Using Hysteresis-Fuzzy Control-Based SMES Unit","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Voltage sag; Flicker; Control theory (sociology); Fuzzy logic; Controller (irrigation); Reliability (semiconductor); Electric power system; Superconducting magnetic energy storage; Power (physics); Computer science; Voltage; Engineering; Reliability engineering; Control engineering; Electronic engineering; Power quality; Electrical engineering; Control (management); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001398249,0.000248642,0.0003365239,0.0002287975,0.0002571944,0.0002503025,0.0004137328,0.0002569153,0.0009499508],"category_scores_gemma":[0.0002676828,0.0001010798,0.0002227795,0.0001695025,0.0001461864,0.0002427394,0.0001931294,0.0002018642,0.0001225816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416069,"about_ca_system_score_gemma":0.0001295847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000963741,"about_ca_topic_score_gemma":0.001502305,"domain_scores_codex":[0.9998935,0.00001806543,0.00001439925,0.00001886828,0.00004425961,0.00001083662],"domain_scores_gemma":[0.9998415,0.00003170949,0.00004256533,0.00001578521,0.00005852554,0.000009963902],"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":[0.001202951,0.000333091,0.003134961,0.0006263176,0.0001384671,0.0009684556,0.0003103464,0.1259831,0.5832306,0.002402868,0.001709707,0.2799591],"study_design_scores_gemma":[0.0001451418,0.001523261,0.007292148,0.00004661123,0.0001503618,0.0006896072,0.0000994113,0.8329877,0.1529076,0.001018325,0.00309054,0.00004920306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.550701,0.0008142004,0.4374617,0.0001925337,0.0001033446,0.00009679103,0.00007269939,0.001494743,0.00906306],"genre_scores_gemma":[0.9927928,0.00004611902,0.006596949,0.0000156033,0.000005710256,0.000009927022,0.00001308887,0.000005028842,0.0005147358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000963741,"threshold_uncertainty_score":0.003177881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004018444919248929,"score_gpt":0.1505981058106601,"score_spread":0.1465796608914111,"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."}}