{"id":"W2108395808","doi":"10.1109/ccece.2007.328","title":"Hybrid Particle Swarm Optimization Approach for Optimal Distribution Generation Sizing and Allocation in Distribution Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Particle swarm optimization; Robustness (evolution); Sizing; Multi-swarm optimization; Computer science; Electric power system; Integer programming; Nonlinear programming; Algorithm; Nonlinear system; Power (physics); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008197316,0.000196563,0.0001803687,0.00004858119,0.0001160008,0.0001304022,0.0000639276,0.0001195763,0.000002532725],"category_scores_gemma":[0.00008493549,0.000219808,0.00003714849,0.0002424804,0.00003147489,0.0005434696,0.00001830794,0.0001055167,0.000001898632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005051602,"about_ca_system_score_gemma":0.00001401457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003424762,"about_ca_topic_score_gemma":0.000008212542,"domain_scores_codex":[0.9985899,0.00002725431,0.0004904238,0.0003033328,0.0001734917,0.0004155613],"domain_scores_gemma":[0.9995078,0.00004878486,0.00005921901,0.0001510321,0.0001321291,0.0001010084],"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":[0.00003232899,0.00005583077,0.0003405364,0.00007610566,0.00001125724,7.272742e-7,0.00003412925,0.9888508,0.005494471,0.00341531,0.0003112392,0.001377208],"study_design_scores_gemma":[0.000659089,0.00006088799,0.0009346247,0.00001362886,0.00002080832,0.00001080029,0.0001301435,0.969379,0.0283508,0.000006018809,0.0001961418,0.000238056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3313049,0.0002234658,0.6672662,0.00001995539,0.000179116,0.0005794903,0.0001652523,0.0001949073,0.0000667265],"genre_scores_gemma":[0.9717685,0.00004471752,0.01515499,0.000006047895,0.0001694957,0.000120665,0.01269527,0.00002665743,0.00001363243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6521112,"threshold_uncertainty_score":0.896351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438861154943931,"score_gpt":0.2221614285581009,"score_spread":0.2077728170086615,"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."}}