{"id":"W4392362231","doi":"10.18280/jesa.570104","title":"Enhanced Incremental Conductance Maximum Power Point Tracking Algorithm for Photovoltaic System in Variable Conditions","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lembaga Penelitian dan Pengabdian Kepada Masyarakat; Universitas Negeri Padang","keywords":"Photovoltaic system; Conductance; Maximum power point tracking; Variable (mathematics); Tracking (education); Maximum power principle; Power (physics); Algorithm; Point (geometry); Computer science; Control theory (sociology); Mathematics; Engineering; Physics; Electrical engineering; Artificial intelligence; Control (management); Thermodynamics","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":[0.001489208,0.000467553,0.0007325552,0.0007518245,0.0004136917,0.0007998726,0.0004545828,0.0002197818,0.0005816382],"category_scores_gemma":[0.0001824253,0.00044426,0.0002692996,0.001001022,0.0001153235,0.001268681,0.00008137195,0.0004811056,0.0001100968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001855581,"about_ca_system_score_gemma":0.0003179339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003604831,"about_ca_topic_score_gemma":0.00005034349,"domain_scores_codex":[0.9961191,0.0003993322,0.001576054,0.0005710513,0.0005907346,0.0007437053],"domain_scores_gemma":[0.9981251,0.0003631389,0.0004694947,0.0003845817,0.0004364103,0.0002212307],"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":[0.00008499996,0.0003899714,0.0001220061,0.002946178,0.0009528619,0.001233246,0.003910385,0.005681664,0.8010127,0.06631695,0.01500492,0.1023441],"study_design_scores_gemma":[0.002862011,0.000607939,0.002486472,0.01201783,0.000217153,0.007292272,0.003455983,0.7179333,0.2210321,0.02084738,0.009705872,0.001541689],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04419406,0.002569637,0.9270273,0.00004133066,0.002745888,0.001913207,0.0004053074,0.002663905,0.01843939],"genre_scores_gemma":[0.8956943,0.00007870192,0.1024234,0.00009618339,0.0002521429,0.0004284928,0.00006306218,0.0001836276,0.0007800907],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8515003,"threshold_uncertainty_score":0.9998009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725877163891455,"score_gpt":0.2695847452984255,"score_spread":0.2523259736595109,"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."}}