{"id":"W4406012017","doi":"10.1109/mpe.2024.3446737","title":"Wide-Area-Measurement-System-Based Event Analytics in the Power System: A Data-Driven Framework for Disturbance Characterization and Source Localization in the Indian Grid","year":2025,"lang":"en","type":"article","venue":"IEEE Power and Energy Magazine","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Disturbance (geology); Analytics; Event (particle physics); Power grid; Characterization (materials science); Grid; Computer science; Electric power system; Event data; Real-time computing; Power (physics); Database; Geography; Geology","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":[0.001018619,0.0006972207,0.0004506454,0.001259497,0.0003991024,0.002556963,0.0009508505,0.0004420891,0.0005219616],"category_scores_gemma":[0.001721955,0.0002826187,0.00039708,0.001447361,0.001426556,0.001743415,0.001328574,0.00116414,0.0002215695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008099264,"about_ca_system_score_gemma":0.001046937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008001601,"about_ca_topic_score_gemma":0.008576111,"domain_scores_codex":[0.9994759,0.0001450144,0.00004296585,0.0001056827,0.0001846884,0.00004568877],"domain_scores_gemma":[0.999384,0.0002051811,0.00009328528,0.0001245536,0.0001425052,0.00005039933],"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":[0.0001962371,0.0002792313,0.02108746,0.000334685,0.0002671957,0.0006616066,0.001049993,0.4250951,0.03384535,0.1771534,0.006963129,0.3330667],"study_design_scores_gemma":[0.000009265382,0.0000810664,0.007505243,0.00005024171,0.00004203384,0.0001611382,0.0003428136,0.9169014,0.007314348,0.05694336,0.01059632,0.00005280366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01941224,0.0004717635,0.9732395,0.001037739,0.0000515453,0.00005392538,0.0002502005,0.001476781,0.004006299],"genre_scores_gemma":[0.8415502,0.0008042976,0.1555597,0.0001858778,0.0001115123,0.00005774128,0.0003150126,0.0001058609,0.001309897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008001601,"threshold_uncertainty_score":0.01591003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268330180650655,"score_gpt":0.2222887236077792,"score_spread":0.2096054218012726,"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."}}