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

doi:10.1002/2050-7038.12590

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid
	Bai, Xiwei1,2 ; Tan, Jie1 ; Ma, Shiying 3; Liu, Daowei 3
发表期刊	INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
ISSN	2050-7038
	2020-10-06
页码	17
通讯作者	Tan, Jie(jie.tan@ia.ac.cn)
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.
关键词	deep neural network grid topology local structures voltage stability margin
DOI	10.1002/2050-7038.12590
关键词[WOS]	SUPPORT VECTOR REGRESSION ; LOAD ; PREDICTION ; COLLAPSE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1701262] ; National Natural Science Foundation of China[U1801263]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000575282700001
出版者	WILEY
七大方向——子方向分类	计算智能
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42057
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Tan, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.China Elect Power Res Inst, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.