{"id":"W2614888260","doi":"10.1049/iet-gtd.2016.2054","title":"Decentralised coordinated secondary voltage control of multi‐area power grids using model predictive control","year":2017,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Control theory (sociology); Robustness (evolution); AC power; Model predictive control; Computer science; Electric power system; Compensation (psychology); Grid; Controller (irrigation); Voltage; Power control; Transmission (telecommunications); Decentralised system; Control engineering; Power (physics); Engineering; Control (management); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002244882,0.0002823592,0.0003689434,0.00006065949,0.0004374802,0.0001283383,0.0002045461,0.0002517297,0.0001350957],"category_scores_gemma":[0.00004468556,0.0002757503,0.0001618097,0.00007948716,0.00007943483,0.0005886958,0.000008270884,0.0001958961,0.000002403781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443449,"about_ca_system_score_gemma":0.00009780887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000203544,"about_ca_topic_score_gemma":0.000005431949,"domain_scores_codex":[0.9985508,0.00005821592,0.0005468058,0.0002933745,0.000231032,0.0003198171],"domain_scores_gemma":[0.9988924,0.00001985507,0.0002125198,0.0003576203,0.0003519293,0.000165737],"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":[0.0001302758,0.00005517334,0.0001449214,0.00002356777,0.00006712774,0.000001450348,0.00006225514,0.6217201,0.3739159,0.00005297584,0.0005935846,0.003232585],"study_design_scores_gemma":[0.006010804,0.00005203566,0.001130753,0.00004740316,0.0001367969,0.000002700536,0.00001184985,0.9293805,0.06249877,0.00002983047,0.0004489796,0.0002496222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04468064,0.0007867232,0.951019,0.0001101082,0.0003352188,0.0006014396,0.002241377,0.0001663428,0.00005907294],"genre_scores_gemma":[0.9970335,0.0001667023,0.001200491,0.00003786279,0.00007770726,0.00003145584,0.001393546,0.00003488489,0.00002386681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9523528,"threshold_uncertainty_score":0.9999695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563591014376353,"score_gpt":0.2273433799911536,"score_spread":0.2117074698473901,"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."}}