{"id":"W2123322784","doi":"10.1109/tpwrd.2011.2119497","title":"An Adaptive Feedforward Compensation for Stability Enhancement in Droop-Controlled Inverter-Based Microgrids","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Voltage droop; Feed forward; Control theory (sociology); Microgrid; Robustness (evolution); Compensation (psychology); Control engineering; Computer science; Engineering; Adaptive control; Voltage; Voltage source; Control (management)","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":[0.0002210368,0.0002358866,0.0003276828,0.000238565,0.00008328292,0.00002753406,0.0001292276,0.0001236668,0.0003599071],"category_scores_gemma":[0.000001779779,0.0002434063,0.0001707269,0.0001693468,0.00003691746,0.0002900522,4.293304e-7,0.0001632638,0.00002101384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212619,"about_ca_system_score_gemma":0.00004340615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007704644,"about_ca_topic_score_gemma":0.0003290411,"domain_scores_codex":[0.9988062,0.00006401896,0.0004180082,0.0002907227,0.0001287407,0.0002923259],"domain_scores_gemma":[0.9993915,0.00007599341,0.00004911173,0.0002751655,0.0001215033,0.0000867183],"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":[0.01209457,0.003057895,0.0001358505,0.0001193542,0.0004513947,0.000006763623,0.004114311,0.7907548,0.1236236,0.00004459876,0.0001424083,0.06545442],"study_design_scores_gemma":[0.006894644,0.0007761262,0.0002809568,0.00003103759,0.00007404503,5.964035e-7,0.0001756201,0.8375106,0.1538486,0.00004153161,0.00006070487,0.0003055209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2385964,0.00007982172,0.7594845,0.00001239324,0.0004813042,0.0009475645,0.0000621577,0.0001506859,0.0001851503],"genre_scores_gemma":[0.9935076,0.00004495633,0.005829766,0.0001264694,0.00001463818,0.0004116504,0.00001935205,0.00003590516,0.000009676905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7549112,"threshold_uncertainty_score":0.992582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325835907078336,"score_gpt":0.2063922488492596,"score_spread":0.1831338897784762,"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."}}