典型文献
Fault Identification in Power Network Based on Deep Reinforcement Learning
文献摘要：
With the integration of alternative energy and re-newables,the issue of stability and resilience of the power net-work has received considerable attention.The basic necessity for fault diagnosis and isolation is fault identification and location.The conventional intelligent fault identification method needs su-pervision,manual labelling of characteristics,and requires large amounts of labelled data.To enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this paper.The proposed method uses different faults as parameters of the model to expand the scope of fault identification.In addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than requiring manual and mechanical tuning of parameters.The methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning methods.The obtained results have confirmed the feasibility and effectiveness of the proposed method.
文献关键词：
中图分类号：
作者姓名：
Mengshi Li;Huanming Zhang;Tianyao Ji;Q.H.Wu
作者机构：
School of Electric Power Engineering,South China University of Technology,Guangzhou 510641,China
文献出处：
引用格式：
[1]Mengshi Li;Huanming Zhang;Tianyao Ji;Q.H.Wu-.Fault Identification in Power Network Based on Deep Reinforcement Learning)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(03):721-731
A类：
newables,pervision
B类：
Fault,Identification,Power,Network,Based,Deep,Reinforcement,Learning,With,integration,alternative,energy,issue,stability,resilience,power,has,received,considerable,attention,basic,necessity,diagnosis,isolation,identification,location,conventional,needs,manual,labelling,characteristics,requires,large,amounts,labelled,data,To,enhance,methods,get,rid,dependence,novel,deep,reinforcement,learning,DRL,which,not,enough,field,investigated,this,paper,proposed,uses,different,faults,parameters,model,expand,scope,In,addition,algorithm,can,intelligently,modify,according,observations,obtained,from,network,environment,rather,than,requiring,mechanical,tuning,methodology,was,tested,IEEE,bus,several,scenarios,performance,compared,that,population,optimization,supervised,results,have,confirmed,feasibility,effectiveness
AB值：
0.561923
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