Residential College | false |
Status | 已發表Published |
A Modified Data-driven Power Flow Model for Power Estimation with Incomplete Bus Data | |
Zheng Xing1; Keng-Weng Lao1; HongJun Gao2; NingYi Dai1 | |
2022 | |
Conference Name | 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) |
Source Publication | Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022 |
Pages | 316-320 |
Conference Date | FEB 25-27, 2022 |
Conference Place | Ritsumeikan Univ, Shiga, JAPAN |
Country | JAPAN |
Publisher | IEEE |
Abstract | For power estimation in power systems, the power flow formulation is the most commonly used method. Data-driven approach is a newly proposed technology, unlike traditional model-driven power flow calculations, it can bypass circuit parameters and environmental variables to obtain results from global bus data. However, with the development of renewable energy, more complex power grids make it difficult to obtain complete data and reduce performance. Therefore, this paper proposes a method of estimating power from incomplete bus data. The method is based on a modified data-driven power flow model, and its core is to derive and induce a regressive model through the physical model. The simulation results on IEEE standard bus 30 show that this method has higher accuracy than other models in incomplete bus data power estimation. |
Keyword | Data-driven Incomplete Data Power Estimation Power Flow Model |
DOI | 10.1109/CPEEE54404.2022.9738684 |
URL | View the original |
Indexed By | CPCI-S |
Language | 英語English |
WOS Research Area | Construction & Building Technology ; Engineering |
WOS Subject | Construction & Building Technology ; Engineering, Electrical & Electronic |
WOS ID | WOS:000814732300055 |
Scopus ID | 2-s2.0-85127977665 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) |
Affiliation | 1.University of Macau, State Key Laboratory of Internet of Things for Smart City, Department of Electrical and Computer Engineering, Macao, Macao 2.Sichuan University, College of Electrical Engineering, Sichuan, China |
First Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Zheng Xing,Keng-Weng Lao,HongJun Gao,et al. A Modified Data-driven Power Flow Model for Power Estimation with Incomplete Bus Data[C]:IEEE, 2022, 316-320. |
APA | Zheng Xing., Keng-Weng Lao., HongJun Gao., & NingYi Dai (2022). A Modified Data-driven Power Flow Model for Power Estimation with Incomplete Bus Data. Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022, 316-320. |
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