{"id":"W2785950534","doi":"10.1109/epec.2017.8286201","title":"Parameter validation for Kalman filter based dynamic state estimation of power plant dynamics","year":2017,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; McGill University","funders":"","keywords":"Kalman filter; Phasor measurement unit; Control theory (sociology); Extended Kalman filter; Parametric statistics; Phasor; Benchmark (surveying); Invariant extended Kalman filter; Computer science; Fast Kalman filter; Monte Carlo method; Alpha beta filter; Ensemble Kalman filter; Unscented transform; Electric power system; Engineering; Power (physics); Mathematics; Statistics; Moving horizon estimation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003718655,0.000676059,0.0007647998,0.0005654187,0.0004364894,0.0009840769,0.0006239495,0.000955772,0.001325493],"category_scores_gemma":[0.01743434,0.0003525796,0.0005545378,0.0004269516,0.0004395054,0.001266662,0.0006831589,0.001025173,0.0003720543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005892192,"about_ca_system_score_gemma":0.001340677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167969,"about_ca_topic_score_gemma":0.006901266,"domain_scores_codex":[0.9989036,0.0004446568,0.0001059896,0.0001695446,0.0002628815,0.0001133028],"domain_scores_gemma":[0.9919219,0.005468483,0.0005904866,0.0007498311,0.001214827,0.00005442222],"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":[0.0003052629,0.00009244982,0.005510308,0.0002710636,0.0001168944,0.00007689295,0.0001607197,0.9271489,0.006886454,0.002293818,0.00036909,0.05676815],"study_design_scores_gemma":[0.00001694472,0.0001100674,0.002617084,0.00003934984,0.0000154309,0.00002479757,0.00002901296,0.9897873,0.006272909,0.000641054,0.0004252146,0.00002078295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2296066,0.0007019276,0.7644475,0.0001829743,0.00007533942,0.00008977769,0.0003598914,0.001794162,0.002741919],"genre_scores_gemma":[0.9773045,0.0001202326,0.02170755,0.00002118834,0.000005202597,0.00006188307,0.0003279273,0.00005285593,0.0003986107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01167969,"threshold_uncertainty_score":0.0232234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128770121839739,"score_gpt":0.2474864058002251,"score_spread":0.2346093936162512,"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."}}