{"id":"W4316660408","doi":"10.1109/naps56150.2022.10012264","title":"Compartive Study of Data-Driven Dynamic Load Model Identification Methods Based on Simulated and Actual PMU Data","year":2022,"lang":"en","type":"article","venue":"2022 North American Power Symposium (NAPS)","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Kalman filter; Control theory (sociology); Computer science; Identification (biology); Electric power system; Nonlinear system; Stability (learning theory); Process (computing); Power (physics); Artificial intelligence","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":[0.0009101859,0.0002875681,0.0005518088,0.0001984842,0.0001968728,0.000049211,0.001180516,0.00002533933,0.0001368009],"category_scores_gemma":[0.00006877185,0.0003157798,0.00003885923,0.0009203549,0.000124957,0.00033049,0.0007758097,0.0003048902,0.000004160146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002548831,"about_ca_system_score_gemma":0.000127828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001406333,"about_ca_topic_score_gemma":0.0003106505,"domain_scores_codex":[0.9969064,0.0006117347,0.0006997848,0.0008574225,0.0006330695,0.0002915668],"domain_scores_gemma":[0.9965475,0.0002377879,0.000289018,0.002673091,0.0001118635,0.0001407183],"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":[0.0001204986,0.0005041996,0.003892467,0.0000259368,0.0001301714,0.000003337138,0.002845665,0.9904544,0.0007055926,0.000002099874,0.0006861685,0.0006294948],"study_design_scores_gemma":[0.0007117311,0.0003478561,0.00766745,0.000003221619,0.00009011637,0.000001453818,0.001702631,0.9880613,0.00001677236,6.672864e-7,0.001107598,0.0002891782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5836463,0.00006891379,0.4083751,0.0001425462,0.0004582397,0.001417913,0.004911993,0.0003598623,0.0006191967],"genre_scores_gemma":[0.9946968,0.00001043978,0.002803361,0.00008852481,0.000004176226,0.00004575893,0.002255324,0.00005355141,0.00004206057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4110505,"threshold_uncertainty_score":0.9999294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03076061958590678,"score_gpt":0.3211066169785707,"score_spread":0.2903459973926639,"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."}}