摘要
Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.
作者简介
Corresponding author:Haosen Yang received the B.S.degree from South China University of Technology,Guangzhou,China,in 2017,and M.S.degree in Shanghai Jiaotong University,Shanghai,China,in 2019.He is pursuing the Ph.D.degree in the School of Electronics and Electrical Engineering,Shanghai Jiaotong University.His research interests include voltage stability and state estimation of power grid,machine learning and data science.e-mail:31910019@sjtu.edu.cn;Robert C.Qiu received the Ph.D.degree in electrical engineering from New York University,New York,USA.He currently serves as a Professor in the Research Center for Big Data Engineering and Technologies,State Energy Smart Grid R&D Center,Department of Electronics and Electrical Engineering,Shanghai Jiaotong University,Shanghai,China.He was with GTE Laboratories,Inc.,Waltham and Bell Labs,Lucent Technologies,Southern Tier,New York,USA.He was the Founder Chief Executive Officer and the President of Wiscom Technologies,Inc.,manufacturing and marketing wideband code division multiple access(WCDMA)chipsets,Murray Hill,New Jersey,USA.In 2008,he became a Professor at the Center for Manufacturing Research,Department of Electrical and Computer Engineering,Tennessee Technological University,Cookeville,USA.He was named a Fellow of the Institute of Electrical and Electronics Engineers(IEEE)in 2015 for his contributions to ultra-wideband wireless communications.His current research interests include wireless communication and networking,random matrix theory based theoretical analysis for deep learning,and smart grid.e-mail:rcqiu@sjtu.edu.cn;Houjie Tong received the B.S.degree in North China Electric Power University,Beijing,China,in 2017.He is currently pursuing the M.S.degree in Shnaghai Jiaotong University,Shanghai,China.His research interests include graph neural networks and power system stability.e-mail:thj_926@sjtu.edu.cn
