LAPSE:2023.21536
Published Article
LAPSE:2023.21536
Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources
March 22, 2023
Abstract
The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.
The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.
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Keywords
Distributed Energy Resources (DERs), distribution systems, fault localization, kernel density estimation (KDE), Support Vector Data Description (SVDD)
Suggested Citation
Lin Z, Duan D, Yang Q, Hong X, Cheng X, Yang L, Cui S. Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources. (2023). LAPSE:2023.21536
Author Affiliations
Lin Z: Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China; Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; School of Informatics, Xiamen University, Xiamen 361005, China [ORCID]
Duan D: Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA [ORCID]
Yang Q: School of Informatics, Xiamen University, Xiamen 361005, China
Hong X: School of Informatics, Xiamen University, Xiamen 361005, China
Cheng X: Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; School of Electronics Engineering and Computer Science, Peking University, Beijing 100080, China
Yang L: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Cui S: Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China; Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; Department of Electrical and Computer Engineering, University of Califor
Duan D: Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA [ORCID]
Yang Q: School of Informatics, Xiamen University, Xiamen 361005, China
Hong X: School of Informatics, Xiamen University, Xiamen 361005, China
Cheng X: Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; School of Electronics Engineering and Computer Science, Peking University, Beijing 100080, China
Yang L: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Cui S: Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China; Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China; Department of Electrical and Computer Engineering, University of Califor
Journal Name
Energies
Volume
13
Issue
1
Article Number
E275
Year
2020
Publication Date
2020-01-06
ISSN
1996-1073
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PII: en13010275, Publication Type: Journal Article
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LAPSE:2023.21536
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https://doi.org/10.3390/en13010275
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Mar 22, 2023
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