{"id":"W2926588219","doi":"10.1049/iet-stg.2018.0095","title":"Wind turbine participation in micro‐grid frequency control through self‐tuning, adaptive fuzzy droop in de‐loaded area","year":2019,"lang":"en","type":"article","venue":"IET Smart Grid","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Voltage droop; Control theory (sociology); Wind power; Context (archaeology); Turbine; Automatic frequency control; Inertia; Fuzzy logic; Frequency grid; Computer science; Wind speed; Grid; Renewable energy; Adaptive neuro fuzzy inference system; Engineering; Fuzzy control system; Control (management); Mathematics; Voltage; Meteorology; Physics","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":[0.000169267,0.0002846297,0.0002100485,0.0001221273,0.0001458135,0.0004375823,0.0003205011,0.0002033973,0.0004459922],"category_scores_gemma":[0.0003171624,0.00008817881,0.0001499499,0.0001159448,0.0002006618,0.0002564603,0.0001975932,0.0002190501,0.00007804886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164749,"about_ca_system_score_gemma":0.000127048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654276,"about_ca_topic_score_gemma":0.0021688,"domain_scores_codex":[0.9999168,0.00001745603,0.000006135093,0.0000253824,0.00002399852,0.00001034571],"domain_scores_gemma":[0.9998935,0.00003491607,0.00002559718,0.00001151493,0.00002861078,0.000005773166],"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":[0.0003467783,0.0001168813,0.001973809,0.0001822891,0.00007225989,0.0002991297,0.0002052761,0.8270169,0.04949506,0.00353492,0.0005600309,0.1161967],"study_design_scores_gemma":[0.0000119195,0.00009755237,0.0007053033,0.000005588325,0.00001498743,0.00002735547,0.00002371529,0.9942513,0.003957095,0.0004038227,0.000497109,0.000004376094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4389607,0.0004306788,0.5519478,0.00009833031,0.0000695515,0.00006434106,0.00002853914,0.000266758,0.008133284],"genre_scores_gemma":[0.9949238,0.00004661263,0.004529072,0.0000069033,0.000005173272,0.000008844742,0.000005981423,0.000003549877,0.000470028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001654276,"threshold_uncertainty_score":0.003289282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008513598539378959,"score_gpt":0.2090559149702447,"score_spread":0.2005423164308658,"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."}}