{"id":"W1997791509","doi":"10.1109/naps.2007.4402374","title":"RLS and Kalman Filter Identifiers Based Adaptive SVC Controller","year":2007,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Kalman filter; Controller (irrigation); Recursive least squares filter; Computer science; Static VAR compensator; Electric power system; Identifier; Estimator; Extended Kalman filter; Identification (biology); System identification; Control engineering; Power (physics); Adaptive filter; Engineering; Mathematics; Algorithm; Data modeling; 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":[0.000555705,0.0005059275,0.0005430976,0.0003925567,0.0002771341,0.0006184446,0.0007714454,0.0005301034,0.002393055],"category_scores_gemma":[0.001315897,0.0002115236,0.0003415665,0.0002532771,0.0003731827,0.0005246178,0.0003074929,0.0006342388,0.000691975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003640014,"about_ca_system_score_gemma":0.0004732244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004107094,"about_ca_topic_score_gemma":0.003552379,"domain_scores_codex":[0.9995465,0.0000807602,0.00003905171,0.0001021012,0.0002007178,0.00003079235],"domain_scores_gemma":[0.9994179,0.0002057677,0.00007281028,0.00006244935,0.0002273522,0.0000137819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004124442,0.0001604784,0.001457957,0.0005039245,0.0001801989,0.0002299394,0.0002862838,0.4427552,0.045423,0.01848307,0.004551906,0.4855556],"study_design_scores_gemma":[0.00003425011,0.0001224331,0.0004554949,0.00001561992,0.00002794962,0.00006192425,0.000009585937,0.9884416,0.006039715,0.0009628945,0.003813282,0.0000153261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01366965,0.0006246277,0.9768848,0.0001223703,0.0001421623,0.00005476143,0.0000394393,0.002278741,0.006183355],"genre_scores_gemma":[0.9118156,0.0005892945,0.07963267,0.0001231467,0.0001178591,0.0001531694,0.0001262805,0.00009661959,0.007345442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004107094,"threshold_uncertainty_score":0.008166373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026494448918331,"score_gpt":0.2090864184962913,"score_spread":0.198821474007108,"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."}}