{"id":"W4200294392","doi":"10.1109/tnnls.2021.3133350","title":"Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Robustness (evolution); Computer science; Residual; Electric power system; Artificial neural network; Convolutional neural network; Control theory (sociology); Artificial intelligence; Power (physics); Algorithm; Control (management)","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":[0.0003539987,0.0008110165,0.0005730194,0.0003803581,0.0002283231,0.0004994748,0.0009006989,0.0005283568,0.0009852927],"category_scores_gemma":[0.0006450544,0.0003630392,0.0005032452,0.0003539703,0.000278851,0.001033889,0.0005974532,0.0009004544,0.0002724838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005065217,"about_ca_system_score_gemma":0.0005401403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065622,"about_ca_topic_score_gemma":0.01063433,"domain_scores_codex":[0.9998479,0.00002657861,0.000008987531,0.00004441683,0.00004441179,0.00002766285],"domain_scores_gemma":[0.9998302,0.00004730138,0.00003036239,0.00002214545,0.00005759847,0.00001227739],"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":[0.00007960809,0.00005825574,0.00120048,0.00003897136,0.00005926045,0.00006963409,0.00004282735,0.8974854,0.004411526,0.00217457,0.0009420731,0.09343732],"study_design_scores_gemma":[8.52175e-7,0.000005800966,0.00007121846,8.50389e-7,0.000001808392,0.000002698415,0.000001212331,0.9993837,0.0002090685,0.0002641364,0.00005724311,0.000001389462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06721228,0.0004727301,0.928301,0.0001790326,0.0000407194,0.00002417427,0.00008367358,0.001325377,0.002361001],"genre_scores_gemma":[0.9515901,0.0002044894,0.04500752,0.00007978221,0.00002543901,0.00005068437,0.0002521079,0.0000563117,0.002733646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01065622,"threshold_uncertainty_score":0.02118838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480123278529094,"score_gpt":0.2181573463414601,"score_spread":0.2033561135561692,"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."}}