{"id":"W3096943283","doi":"10.18280/jesa.530413","title":"Design of Load Frequency Controller for Multi-area System Using AI Techniques","year":2020,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Controller (irrigation); Artificial neural network; Automatic frequency control; Electric power system; Frequency deviation; PID controller; Fuzzy logic; Control engineering; Computer science; Engineering; Power (physics); Control (management); Temperature control; Artificial intelligence; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009982066,0.0004702684,0.001107461,0.0002109099,0.0001939061,0.0002039812,0.0005808087,0.0002103356,0.0000195404],"category_scores_gemma":[0.0004124561,0.0004207012,0.0003239852,0.0003630513,0.00009761401,0.0005395858,0.00003453553,0.000396899,0.00001661673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000896015,"about_ca_system_score_gemma":0.0002261038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003936332,"about_ca_topic_score_gemma":0.000002014671,"domain_scores_codex":[0.9965909,0.0003406313,0.001622751,0.0002956377,0.0005560079,0.0005941245],"domain_scores_gemma":[0.9978471,0.0002260759,0.0005538685,0.0002908602,0.0007737025,0.0003083461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001918024,0.0001110247,0.001053536,0.007917679,0.001867778,0.0003748961,0.004002394,0.150194,0.7864421,0.002015152,0.00719596,0.03863364],"study_design_scores_gemma":[0.001742592,0.0003058519,0.0004482011,0.001335752,0.0001743622,0.0007071533,0.0001526202,0.9876781,0.006532131,0.000195511,0.000291636,0.0004361032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00350368,0.006124191,0.9860434,0.00005516267,0.0008620255,0.001468981,0.0000458806,0.001288921,0.0006077529],"genre_scores_gemma":[0.8268169,0.00004962142,0.1724087,0.00007793315,0.0003749909,0.00007338603,0.000001102249,0.0001684383,0.00002900965],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8374841,"threshold_uncertainty_score":0.9998245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05304224215979967,"score_gpt":0.2683066295438842,"score_spread":0.2152643873840845,"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."}}