{"id":"W4412807755","doi":"10.1007/978-981-96-1264-2_37","title":"Control of Frequency Deviation and Area Control Error in LFC Using Genetic Algorithm-Tuned FOPID by Minimizing Performance Index","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Genetic algorithm; Index (typography); Automatic frequency control; Control (management); Algorithm; Computer science; Control theory (sociology); Mathematics; Telecommunications; Artificial intelligence; Machine learning","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":[0.0004501912,0.0006386094,0.001511167,0.0004203097,0.00005537002,0.00008189865,0.0001917534,0.001030413,0.000005227444],"category_scores_gemma":[0.00005018821,0.0006336673,0.00008953315,0.0001420376,0.00008754502,0.0001141932,0.00002250098,0.000738559,2.868405e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002723009,"about_ca_system_score_gemma":0.00005746632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004680895,"about_ca_topic_score_gemma":0.0002138962,"domain_scores_codex":[0.9972625,0.0001063783,0.001356173,0.0005139593,0.000267665,0.0004933059],"domain_scores_gemma":[0.9985021,0.0005641257,0.0003800588,0.0003655265,0.0001040947,0.0000841367],"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":[0.00004111652,0.000009186066,0.0134069,0.001556862,0.0002820068,0.00002687002,0.0001746544,0.9653038,0.0004201258,0.0001890725,0.00004902655,0.01854033],"study_design_scores_gemma":[0.002157207,0.00006414394,0.0004602227,0.003658948,0.0001030357,0.0000309703,0.000005219129,0.9924403,0.000008213736,0.0001984039,0.000357279,0.0005160557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002832419,0.1560357,0.8355328,0.00001609287,0.001869695,0.001648249,0.0001029276,0.00007100736,0.001891079],"genre_scores_gemma":[0.9978155,0.001009122,0.0004385655,0.00004529352,0.0003411035,0.00007974871,0.00002673108,0.00009226782,0.0001516643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9949831,"threshold_uncertainty_score":0.9996114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008356959953318102,"score_gpt":0.1943472514059571,"score_spread":0.185990291452639,"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."}}