{"id":"W3174548240","doi":"10.18280/jesa.540302","title":"Maximum Power Point Tracking in the Photovoltaic Module Using Incremental Conductance Algorithm with Variable Step Length","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Maximum power point tracking; Maximum power principle; Photovoltaic system; Tracking (education); Correctness; Power (physics); Algorithm; Computer science; Control theory (sociology); Point (geometry); Variable (mathematics); Power optimizer; Limit (mathematics); Electronic engineering; Mathematics; Engineering; Control (management); Electrical engineering; Artificial intelligence; Physics","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":[0.001582616,0.0004363078,0.0006151893,0.000288747,0.0005011454,0.0006922988,0.0005845279,0.0001478221,0.0006404152],"category_scores_gemma":[0.000222004,0.0003217241,0.0001353679,0.001245112,0.0001390177,0.001080001,0.0001230103,0.0006520048,0.000021231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007926332,"about_ca_system_score_gemma":0.0003974954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001096553,"about_ca_topic_score_gemma":0.0001918058,"domain_scores_codex":[0.9955788,0.001166181,0.001102447,0.0004754286,0.0009884926,0.0006886969],"domain_scores_gemma":[0.9978089,0.0001907156,0.0006777364,0.0006168829,0.0005560305,0.0001496807],"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":[0.0003140369,0.00235681,0.01407625,0.000918909,0.001814829,0.01514211,0.01693408,0.07828411,0.6921441,0.0134442,0.006826105,0.1577445],"study_design_scores_gemma":[0.005918594,0.0008143454,0.03769884,0.004853614,0.0003184723,0.06060041,0.01240335,0.7250981,0.1295193,0.01317165,0.007330707,0.002272661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5405575,0.002257146,0.4379579,0.0001135212,0.0007351337,0.001039917,0.0000521918,0.0006546641,0.01663206],"genre_scores_gemma":[0.7140583,0.00009844104,0.284895,0.0003987678,0.0001360372,0.00003742451,0.00001333576,0.0001207793,0.0002419597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.646814,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231018813417999,"score_gpt":0.2602414915790938,"score_spread":0.2371396102372939,"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."}}