{"id":"W2736676561","doi":"10.3390/en10071013","title":"Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation","year":2017,"lang":"en","type":"article","venue":"Energies","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Population and Public Health; King Saud University","keywords":"Renewable energy; Particle swarm optimization; Photovoltaic system; Wind power; Automotive engineering; Distributed generation; Sensitivity (control systems); Electric power system; Computer science; Engineering; Power (physics); Electrical engineering; Electronic engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001109094,0.000145519,0.0001707876,0.0000321615,0.0002207077,0.0001363518,0.0001798459,0.00005821044,0.00006343285],"category_scores_gemma":[0.00008639962,0.0001595662,0.00005692584,0.00006336354,0.00006810499,0.0005821876,0.00005781292,0.00005734603,0.000005641131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009225894,"about_ca_system_score_gemma":0.00002133289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008602338,"about_ca_topic_score_gemma":0.000008381621,"domain_scores_codex":[0.9991754,0.00001650863,0.0002519383,0.0001585935,0.0001610428,0.0002365307],"domain_scores_gemma":[0.9993008,0.00001059307,0.00009378741,0.0004376675,0.0001024465,0.00005465321],"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":[0.000008837259,0.00001886632,0.000175565,0.00001142574,0.00002466163,0.000002868042,0.00003509868,0.9394358,0.05914027,0.00006659881,0.0009790028,0.0001009666],"study_design_scores_gemma":[0.0001864958,0.00002077632,0.0002107035,0.00001166487,0.00001622446,0.000002555971,0.0000174556,0.6526553,0.3465551,0.000006059234,0.0002059382,0.0001116925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6908278,0.0001191134,0.3080977,0.00002192652,0.0003931718,0.00005747943,0.0001301834,0.0001217777,0.0002308886],"genre_scores_gemma":[0.9703686,0.00003360304,0.02913759,0.000003310475,0.0001004382,0.000007221648,0.0002902766,0.00002702112,0.00003190223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2874149,"threshold_uncertainty_score":0.6506922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826668283296588,"score_gpt":0.2344131331876488,"score_spread":0.216146450354683,"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."}}