Data-driven fault localization in distribution systems with distributed energy resources

Zhidi Lin, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang, Shuguang Cui

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Article number275
JournalEnergies
Volume13
Issue number1
DOIs
StatePublished - Jan 6 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by Shenzhen Fundamental Research Fund under Grant No. JCYJ20170411102217994 and Guangdong province under grant No. 2017ZT07X152.

Funding Information:
Funding: This work was supported in part by Shenzhen Fundamental Research Fund under Grant No. JCYJ20170411102217994 and Guangdong province under grant No. 2017ZT07X152.

Keywords

  • Distributed Energy Resources (DERs)
  • Distribution systems
  • Fault localization
  • Kernel density estimation (KDE)
  • Support Vector Data Description (SVDD)

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