{"id":"W3026273933","doi":"10.35833/mpce.2019.000163","title":"Data-driven Operation Risk Assessment of Wind-integrated Power Systems via Mixture Models and Importance Sampling","year":2020,"lang":"en","type":"article","venue":"Journal of Modern Power Systems and Clean Energy","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; SaskPower","keywords":"Wind power; Reliability engineering; Electric power system; Monte Carlo method; Computer science; Reliability (semiconductor); Wind speed; Computation; Cross entropy; Cluster analysis; Entropy (arrow of time); Engineering; Power (physics); Principle of maximum entropy; Statistics; Algorithm; Meteorology","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":[0.002555994,0.0009091699,0.0009789463,0.001077048,0.0003229389,0.0009741195,0.001265968,0.0005594784,0.000639203],"category_scores_gemma":[0.007390795,0.000712794,0.0009008796,0.0007180945,0.0006002082,0.001553518,0.001166058,0.001057389,0.00009797067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007984199,"about_ca_system_score_gemma":0.0008582612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00429881,"about_ca_topic_score_gemma":0.003601753,"domain_scores_codex":[0.998799,0.0005638068,0.00005643622,0.0001431518,0.0003664907,0.00007115963],"domain_scores_gemma":[0.9963806,0.002549381,0.0003206934,0.0002253994,0.0004148443,0.0001090587],"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":[0.00006051137,0.00003470849,0.001624267,0.00003407055,0.0000524394,0.00004844564,0.00003613138,0.9713132,0.0007541655,0.008718672,0.0001259687,0.01719737],"study_design_scores_gemma":[0.000001134763,0.000005403645,0.00008588716,0.000001142164,0.000002009693,0.000003997555,0.000001530062,0.9986841,0.0001154556,0.001065205,0.0000323126,0.000001840562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02224988,0.00008900333,0.9771136,0.00004365344,0.00001010398,0.00002960087,0.00002412503,0.00008557775,0.0003544129],"genre_scores_gemma":[0.8363756,0.0002362484,0.1619124,0.00003579752,0.00004348516,0.0001553045,0.0002293016,0.00004973949,0.0009621079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00429881,"threshold_uncertainty_score":0.01351756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302556683721001,"score_gpt":0.2407874228609672,"score_spread":0.2177618560237572,"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."}}