Knowledge Commons of Institute of Automation,CAS
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, Shiying3; Liu, Daowei3 | |
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. |
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