典型文献
A Super-resolution Perception-based Incremental Learning Approach for Power System Voltage Stability Assessment with Incomplete PMU Measurements
文献摘要：
This paper develops a fully data-driven,missing-data tolerant method for post-fault short-term voltage stability(STVS) assessment of power systems against the incomplete PMU measurements.The super-resolution perception (SRP),based on a deep residual learning convolutional neural network,is employed to cope with the missing PMU measurements.The incremental broad learning (BL) is used to rapidly update the model to maintain and enhance the online application performance.Being different from the state-of-the-art methods,the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change sce-nario.Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.
文献关键词：
中图分类号：
作者姓名：
Chao Ren;Yan Xu;Junhua Zhao;Rui Zhang;Tong Wan
作者机构：
Interdisciplinary Graduate School,Nanyang Technolog-ical University,Singapore;School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore;School of Science and Engineering,The Chinese University of Hong Kong (Shenzhen),China;Changsha University of Science and Technology,Changsha 410114,China;School of Electrical and Information Engineering,University of Sydney,Sydney,Australia
文献出处：
引用格式：
[1]Chao Ren;Yan Xu;Junhua Zhao;Rui Zhang;Tong Wan-.A Super-resolution Perception-based Incremental Learning Approach for Power System Voltage Stability Assessment with Incomplete PMU Measurements)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(01):76-85
A类：
STVS
B类：
Super,resolution,Perception,Incremental,Learning,Approach,Power,System,Voltage,Stability,Assessment,Incomplete,PMU,Measurements,This,paper,develops,fully,data,driven,missing,tolerant,post,fault,short,voltage,stability,assessment,power,systems,against,incomplete,measurements,super,perception,SRP,deep,residual,learning,convolutional,neural,network,employed,cope,incremental,broad,BL,used,rapidly,update,model,maintain,enhance,online,application,performance,Being,different,from,state,art,methods,proposed,can,fill,under,any,placement,information,loss,topology,change,sce,nario,Simulation,results,demonstrate,that,has,best,terms,accuracy,tolerance,among,existing,benchmark,testing
AB值：
0.655354
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