{"id":"W1981074152","doi":"10.1109/tpwrs.2012.2207972","title":"Implementing Virtual Inertia in DFIG-Based Wind Power Generation","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":455,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control theory (sociology); Inertia; Wind power; Electric power system; Islanding; Capacitor; Computer science; Energy storage; Power (physics); Engineering; Renewable energy; Control engineering; Distributed generation; Voltage; Electrical engineering; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002852374,0.0001783881,0.0001745655,0.0002354955,0.00009211971,0.00006246541,0.00007197804,0.0001061022,0.0001860216],"category_scores_gemma":[0.000001740971,0.0001829952,0.00006736135,0.0002291295,0.000009213239,0.0003097819,4.856959e-7,0.0001669459,0.00007927851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001293858,"about_ca_system_score_gemma":0.00001560367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005164455,"about_ca_topic_score_gemma":0.00007567797,"domain_scores_codex":[0.998823,0.00005533721,0.0003745766,0.0001462978,0.0001619013,0.0004388424],"domain_scores_gemma":[0.9996266,0.00002769961,0.00003700623,0.0001921521,0.00003354725,0.00008292913],"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":[0.00001063415,0.00007547556,0.0001620363,0.00001273413,0.00003238956,0.000001062565,0.0004761658,0.9786262,0.01961537,0.00003571487,0.0003261287,0.0006261251],"study_design_scores_gemma":[0.001609283,0.0001092762,0.0004432812,0.00007320328,0.00003708846,0.000009488685,0.0003200611,0.9585901,0.02232454,4.087324e-7,0.01600154,0.0004817193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032066,0.0005488153,0.8916325,0.0000221279,0.003414982,0.0003283262,0.00003121187,0.0001994982,0.0006159747],"genre_scores_gemma":[0.9995558,0.00001152159,0.0001258532,0.00004610016,0.00006012967,0.00005469588,0.00001385658,0.00004187961,0.00009020905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8963492,"threshold_uncertainty_score":0.7462326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008684574244633,"score_gpt":0.2062978219833697,"score_spread":0.1962109762409233,"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."}}