摘要
The real-time transient stability assessment(TSA)and emergency control are effective measures to suppress accident expansion,prevent system instability,and avoid large-scale power outages in the event of power system failure.However,real-time assessment is extremely demanding on computing speed,and the traditional method is not competent.In this paper,an improved deep belief network(DBN)is proposed for the fast assessment of transient stability,which considers the structural characteristics of power system in the construction of loss function.Deep learning has been effective in many fields,but usually is considered as a black-box model.From the perspective of machine learning interpretation,this paper proposes a local linear interpreter(LLI)model,and tries to give a reasonable interpretation of the relationship between the system features and the assessment result,and illustrates the conversion process from the input feature space to the high-dimension representation space.The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China.The result demonstrates that the proposed method has rapidity,high accuracy and good interpretability in transient stability assessment.
The real-time transient stability assessment(TSA)and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However,real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network(DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter(LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.
基金
supported by National Natural Science Foundation of China(No.51777104)
the Science and Technology Project of the State Grid Corporation of China.
作者简介
Shuang Wu received his B.S.degree in electrical engineering from Huazhong University of Science and Technology,Wuhan,China,in 2016,and he is now an Ph.D.candidate in Tsinghua University,Beijing,China.His research interests include power system analysis and control,big data technology in power system.e-mail:523652867@qq.com;Le Zheng received the B.S.and Ph.D.degrees in electrical engineering from Tsinghua University,Beijing,China,in 2011 and 2017,respectively.He is currently a postdoctoral research fellow in Stanford University,Stanford,USA.His research interests include power system stability and control,large-scale wind energy integration,data mining and machine learning applications in power systems.e-mail:zhengl07@stanford.edu;Corresponding author:Wei Hu,received the B.S.and Ph.D.degrees in electrical engineering from Tsinghua University,Beijing,China,in 1998 and 2002,respectively,and he is now an Associate Professor at there.His research interests include power system analysis and control,big data technology in power system,multitype power generator-grid coordination and control,optimization control between renewable energy and energy storage system.e-mail:huwei@mail.tsinghua.edu.cn;Rui Yu received the B.S.and the M.A.degrees in electrical engineering from Chongqing University,Chongqing,China,in 1999 and 2002,respectively,and he is now a senior engineer in Southwest Branch of State Grid Corporation of China,Chengdu,China.His research field is dispatching and operation of power system.e-mail:1458405947@qq.com;Baisi Liu received the B.S.and the M.A.degrees in electrical engineering from Chongqing University,Chongqing,China,in 2002 and 2004,respectively,and he is now a senior engineer in Southwest Branch of State Grid Corporation of China,Chengdu,China.His research field is analysis and control of power system.e-mail:16907860@qq.com
