{"id":"W4391153678","doi":"10.1016/j.epsr.2024.110157","title":"Detection of Cyber-Attacks and Power Disturbances in Smart Digital Substations using Continuous Wavelet Transform and Convolution Neural Networks","year":2024,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolution (computer science); Wavelet transform; Wavelet; Computer science; Convolutional neural network; Continuous wavelet transform; Artificial intelligence; Artificial neural network; Power (physics); Pattern recognition (psychology); Discrete wavelet transform; Electronic engineering; Real-time computing; Engineering; Physics","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":[0.0002950311,0.0002964717,0.0002715626,0.00044245,0.0001170457,0.0003426661,0.000202848,0.0003819354,0.0003549356],"category_scores_gemma":[0.001045215,0.0001082496,0.0002021192,0.0003717061,0.0001984383,0.0005213803,0.0002377391,0.0003391915,0.00007406861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002245441,"about_ca_system_score_gemma":0.0001960566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001866089,"about_ca_topic_score_gemma":0.001914945,"domain_scores_codex":[0.9998722,0.00002137117,0.000008424742,0.00003063137,0.00004383989,0.00002343132],"domain_scores_gemma":[0.9997008,0.0001314813,0.00006349129,0.00002156296,0.00006429646,0.00001837461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001504125,0.0003926229,0.04372847,0.0001358356,0.0001797763,0.0005591079,0.0001500233,0.3462855,0.07090896,0.004754182,0.001349177,0.5300522],"study_design_scores_gemma":[0.000004692665,0.00003496731,0.006121699,0.000002765555,0.000009854945,0.00004592699,0.00001281358,0.9899008,0.003304113,0.0004867991,0.00007224842,0.000003378253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690723,0.000244781,0.328719,0.0001253864,0.0000494863,0.00001649179,0.00006864114,0.0002387928,0.001465093],"genre_scores_gemma":[0.9898711,0.00006359944,0.009599815,0.000008995715,0.000008833761,0.000003215404,0.00003430252,0.000004442809,0.0004057279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001866089,"threshold_uncertainty_score":0.003710508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648549526121224,"score_gpt":0.2785577153113825,"score_spread":0.2620722200501702,"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."}}