{"id":"W2951890792","doi":"10.1049/iet-pel.2018.6287","title":"Switch fault diagnosis for boost DC–DC converters in photovoltaic MPPT systems by using high‐gain observers","year":2019,"lang":"en","type":"article","venue":"IET Power Electronics","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Photovoltaic system; Maximum power point tracking; Converters; Control theory (sociology); Fault (geology); Computer science; Boost converter; Residual; Engineering; Voltage; Inverter; Control (management); Algorithm; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003832733,0.0003614901,0.0002975369,0.0001621758,0.0001562728,0.0004503565,0.0002354007,0.0003676339,0.000596297],"category_scores_gemma":[0.000932908,0.000139104,0.0002371385,0.0001041556,0.0002989182,0.0003536915,0.0003099482,0.0005514165,0.00008974187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003417665,"about_ca_system_score_gemma":0.0003395956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150215,"about_ca_topic_score_gemma":0.002009007,"domain_scores_codex":[0.9998399,0.00004092864,0.000008917161,0.0000247404,0.00006833599,0.00001720425],"domain_scores_gemma":[0.9996769,0.0001807162,0.00005727476,0.00002856174,0.00004832701,0.000008081323],"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":[0.0005716681,0.0001547222,0.002389683,0.0003819289,0.00006949462,0.0002167196,0.0004270334,0.6404051,0.09410221,0.008687756,0.0008657638,0.251728],"study_design_scores_gemma":[0.00001522758,0.00009401303,0.0004912837,0.000008261726,0.00001061005,0.00002293628,0.0000126211,0.988996,0.009283998,0.0007024286,0.0003583628,0.000004273801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08388738,0.0002619155,0.9131431,0.0001054296,0.0000394699,0.00003625359,0.0000118234,0.0004384194,0.002076237],"genre_scores_gemma":[0.9886838,0.00006869225,0.01066779,0.000009943281,0.000004624853,0.00001491022,0.000009756095,0.000005625282,0.0005349244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002150215,"threshold_uncertainty_score":0.004275441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147866069052789,"score_gpt":0.2420927550448544,"score_spread":0.2306140943543265,"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."}}