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Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid

文献类型：期刊论文

作者Bai, Xiwei1,2; Tan, Jie2; Ma, Shiying3; Liu, Daowei3
刊名INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
出版日期2020-10-06
页码17
关键词deep neural network grid topology local structures voltage stability margin
ISSN号2050-7038
DOI10.1002/2050-7038.12590
通讯作者Tan, Jie(jie.tan@ia.ac.cn)
英文摘要Objective Online estimation of voltage stability margin (VSM) is critical to the long-term safety and stable operation of power system. In this paper, a deep neural network (DNN)-based model that incorporates the intrinsic grid topology information is proposed to achieve accurate VSM estimation. Methods The node embedding information together with the electrical parameter measurements are combined together into an array of local structures as the input feature vector. A local-global deep neural network (LGDNN) model is proposed to extract and integrate the local information into high-level global representation for VSM estimation. The Node2vec algorithm is employed to obtain embedded node vectors and a NodeRank algorithm is designed to form local structures, which are composed of the measurements of a fixed number of similar nodes according to the similarity among their embedded vectors. A sequential DNN with cascade-connected depthwise separable 1D convolutional layer and fully connected layer is trained to estimate the current VSM. Results The model performance is validated on the IEEE-39 and IEEE-118 benchmark systems under normal and post-contingency situations. Experiment results indicate that the proposed model can achieve VSM estimation with high precision that surpasses seven mainstream approaches in multiple estimation error indices. Conclusion Through the integration of local structures, the proposed LGDNN increases the estimation accuracy with relatively few parameters than various DNN-based models. Thus, it is an practical model for online power grid voltage stability monitoring.
WOS关键词SUPPORT VECTOR REGRESSION ; LOAD ; PREDICTION ; COLLAPSE
资助项目National Natural Science Foundation of China[U1701262] ; National Natural Science Foundation of China[U1801263]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000575282700001
出版者WILEY
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/42056]  
专题综合信息系统研究中心_工业智能技术与系统
通讯作者Tan, Jie
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China
3.China Elect Power Res Inst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Bai, Xiwei,Tan, Jie,Ma, Shiying,et al. Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid[J]. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS,2020:17.
APA Bai, Xiwei,Tan, Jie,Ma, Shiying,&Liu, Daowei.(2020).Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid.INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS,17.
MLA Bai, Xiwei,et al."Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid".INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2020):17.

入库方式： OAI收割

来源：自动化研究所

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