{"id":"W2787677989","doi":"10.1109/epec.2017.8286161","title":"Kalman filter-based maximum power point tracking for PV energy resources supplying DC microgrid","year":2017,"lang":"en","type":"article","venue":"","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maximum power point tracking; Microgrid; Photovoltaic system; Kalman filter; MATLAB; Control theory (sociology); Computer science; Power (physics); Tracking (education); Maximum power principle; Extended Kalman filter; Engineering; Inverter; Artificial intelligence; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003232247,0.0002912601,0.000386864,0.00020471,0.0002937958,0.0004060218,0.0003781082,0.0003239355,0.001140237],"category_scores_gemma":[0.0009422653,0.0002207616,0.0002121016,0.0002835558,0.0001967116,0.0005231418,0.0002514767,0.0003848746,0.0002927578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884707,"about_ca_system_score_gemma":0.0006847836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01253997,"about_ca_topic_score_gemma":0.01077087,"domain_scores_codex":[0.9998728,0.00002596881,0.00001116302,0.00003161759,0.00004357593,0.0000147696],"domain_scores_gemma":[0.9997873,0.0000985054,0.00003411786,0.00001295888,0.00006117921,0.00000608453],"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":[0.0001427224,0.00003474401,0.001189442,0.0001123267,0.00004295635,0.00005105262,0.00009036456,0.8004193,0.008605658,0.002905248,0.001313901,0.1850923],"study_design_scores_gemma":[0.000006741044,0.00002121784,0.000289227,0.000004381928,0.000005731294,0.000007911789,0.000005062463,0.9974413,0.001469907,0.0003683245,0.0003757327,0.000004389067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02313109,0.000249604,0.973404,0.00008143206,0.00003508096,0.00002210192,0.00002922744,0.0007984396,0.002249015],"genre_scores_gemma":[0.9305738,0.0002836218,0.06663559,0.00003816714,0.00002266493,0.00006705771,0.00006280239,0.00003910397,0.002277241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01253997,"threshold_uncertainty_score":0.02493399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02482378342766411,"score_gpt":0.26779616830005,"score_spread":0.2429723848723859,"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."}}