{"id":"W4386088899","doi":"10.1016/j.apenergy.2023.121740","title":"On data-driven modeling and control in modern power grids stability: Survey and perspective","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Huawei Technologies (Canada)","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Distributed generation; Renewable energy; Wind power; Grid; Computer science; Control (management); Smart grid; Photovoltaic system; Control engineering; Engineering; Systems engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001875423,0.0001180144,0.0001694007,0.00009111479,0.00002934219,0.00003030961,0.00008240651,0.00006133975,0.000005852579],"category_scores_gemma":[0.00001719952,0.0001184952,0.000007988649,0.0001455483,0.00001804532,0.0000685322,0.00005257314,0.00008072617,0.00000215733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003814705,"about_ca_system_score_gemma":0.00000808675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003622554,"about_ca_topic_score_gemma":0.0008727167,"domain_scores_codex":[0.9993162,0.00002239734,0.0001268907,0.0002797874,0.00007439427,0.0001802869],"domain_scores_gemma":[0.9995945,0.0001013351,0.00001037549,0.0002286728,0.00002089646,0.00004422242],"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":[0.00004899699,0.00001013716,0.0001555542,0.00000538624,0.00002568368,0.000001552992,0.0002967844,0.9912145,0.001208208,0.003850249,0.0000495751,0.003133356],"study_design_scores_gemma":[0.0007952037,0.00001207917,0.0007296217,0.000003763004,0.000005124309,3.557379e-7,0.0001538975,0.995586,0.00003201235,0.002528494,0.00002915191,0.0001242925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.812105,0.00100474,0.1835837,0.00008186025,0.0001078184,0.0002142559,0.0001831359,0.0003848985,0.002334599],"genre_scores_gemma":[0.9992853,0.0003767882,0.00009786878,0.00006047883,0.00001979559,0.00002267609,0.0001076803,0.00002520225,0.000004176949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1871803,"threshold_uncertainty_score":0.4832093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672403759704212,"score_gpt":0.2104245671194964,"score_spread":0.1937005295224543,"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."}}