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A novel probabilistic framework with interpretability for generator coherency identification
Liu, Fengrui1; Yin, Yikun2; Li, Baitong3
2022-12-01
Source PublicationInternational Journal of Electrical Power and Energy Systems
ISSN0142-0615
Volume143
Abstract

The increase of penetration of renewable energies has posed inevitable challenges to the stability and safety of power system operations, especially in large-scale multi-machine power systems. Emergency control is thereby crucial to avoid catastrophic accidents, and identifying coherent generators is the basis of wide-area control of a multi-machine power system. However, existing approaches are rule-based or rely on shallow machine learning, lacking effectiveness and robustness due to their insufficient ability of pattern mining from system monitoring indicators. To fill the gap, this paper proposes a novel end-to-end generator coherency identification framework, leveraging an improved auto-encoder to comprehensively exploit information of phasor measurement units (PMUs) obtained from wide-area measuring systems (WAMS). The framework jointly trains the feature extraction module and the clustering module to fully explore the shared knowledge and obtain cluster-specific representations. In addition, a visualization component is equipped with the process-agnostic framework for interpretability. Simulated and practical case studies validate the effectiveness of the proposed approach as it outperforms both deep learning baselines and state-of-the-art methods on all datasets under various situations, including observation window size changes, noisy data, or data missing at random.

KeywordGenerator Coherency Identification Interpretability Multi-task Learning Spatial–temporal Auto-encoder Wide-area Measurement
DOI10.1016/j.ijepes.2022.108474
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000835242900003
Scopus ID2-s2.0-85134549368
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLi, Baitong
Affiliation1.State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, 999078, China
2.School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun, Chaoyang, Jilin, 130021, China
3.Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, 999077, Hong Kong
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Fengrui,Yin, Yikun,Li, Baitong. A novel probabilistic framework with interpretability for generator coherency identification[J]. International Journal of Electrical Power and Energy Systems,2022,143.
APA Liu, Fengrui,Yin, Yikun,&Li, Baitong.(2022).A novel probabilistic framework with interpretability for generator coherency identification.International Journal of Electrical Power and Energy Systems,143.
MLA Liu, Fengrui,et al."A novel probabilistic framework with interpretability for generator coherency identification".International Journal of Electrical Power and Energy Systems 143(2022).
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