{"id":"W4304889315","doi":"10.20944/preprints202210.0191.v1","title":"AI Controller for SEPIC Converter of PV Systems","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Control theory (sociology); Maximum power point tracking; Maximum power principle; Computer science; Voltage; Photovoltaic system; Fuzzy logic; Controller (irrigation); Power (physics); Point (geometry); Engineering; Mathematics; Control (management); Electrical engineering; Artificial intelligence; Inverter; Physics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001409839,0.0005182649,0.001364614,0.0003479681,0.0001150404,0.00003267033,0.001182956,0.0005604207,0.003056525],"category_scores_gemma":[0.0003733739,0.0005549681,0.0005573648,0.0001762166,0.00009794396,0.00009786092,0.001805361,0.0006718666,0.0001782801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004227218,"about_ca_system_score_gemma":0.000266676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003751021,"about_ca_topic_score_gemma":0.00002199589,"domain_scores_codex":[0.9958758,0.0004747748,0.001469225,0.001138161,0.0006028165,0.0004392621],"domain_scores_gemma":[0.995975,0.000282609,0.001134673,0.001871613,0.0006057189,0.0001304303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001951477,0.001447179,0.3766036,0.01021855,0.005872532,0.00003714118,0.005636952,0.4153956,0.08748893,0.07619519,0.01789167,0.001261157],"study_design_scores_gemma":[0.006600168,0.0002118279,0.0116809,0.001284292,0.0005838974,0.00003818722,0.0008058175,0.198616,0.234462,0.01168943,0.5313274,0.002700103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.360056,0.003825359,0.3437038,0.001859831,0.02148448,0.04501933,0.003038276,0.008364885,0.212648],"genre_scores_gemma":[0.984652,0.0001120231,0.0004357347,0.0003641064,0.0001945884,0.006681479,0.0002507386,0.000150661,0.007158696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6245959,"threshold_uncertainty_score":0.9996902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09211618838985679,"score_gpt":0.3453221009258973,"score_spread":0.2532059125360405,"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."}}