{"id":"W3014680910","doi":"10.3390/su12072813","title":"Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Reduction (mathematics); Genetic algorithm; Heuristic; Electric power system; Convergence (economics); Differential evolution; Computer science; Algorithm; Power (physics); Engineering; Control theory (sociology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004275597,0.0009878196,0.0007056829,0.001185972,0.0005652899,0.0008834684,0.0007785711,0.001026492,0.002145204],"category_scores_gemma":[0.0009851289,0.0004825783,0.0006604695,0.0007142441,0.0005112942,0.0004580225,0.0003870385,0.0004664326,0.0002047299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160923,"about_ca_system_score_gemma":0.001636629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01452434,"about_ca_topic_score_gemma":0.01167461,"domain_scores_codex":[0.9997986,0.00006416932,0.000007136353,0.00003477545,0.00005765974,0.00003766543],"domain_scores_gemma":[0.9997122,0.0001198561,0.00006044219,0.00001329497,0.00007287021,0.00002146763],"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":[0.00001914608,0.00002714294,0.0002032349,0.00001491698,0.00001177732,0.0000293176,0.00001533171,0.9895364,0.00109341,0.0007523153,0.0002200918,0.008076937],"study_design_scores_gemma":[0.000006597128,0.00001736127,0.00005371092,0.000002381648,0.000004404612,0.000004665299,0.000006138377,0.9992638,0.0002903718,0.0002594393,0.00008904407,0.00000207477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1608782,0.0004513249,0.8233728,0.0003149217,0.00009877975,0.0002210462,0.00008528659,0.0006966623,0.01388101],"genre_scores_gemma":[0.8755206,0.0001693397,0.1215394,0.00005914978,0.00001778987,0.0001441813,0.00007118868,0.00005218825,0.002426218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01452434,"threshold_uncertainty_score":0.02887958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124055050523394,"score_gpt":0.2495495335653317,"score_spread":0.2371440285129923,"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."}}