Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The integration of Distributed Energy Resources (DERs) introduces non-conventional two-way power flows which cannot be captured well by traditional model-based techniques. This brings great challenges to accurately localize faults and initiate correct actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multilevel system regionalization and probabilistic fault detections on all the subregions. The strategy combines the Support Vector Data Description (SVDD) and the Kernel Density Estimation (KDE) to provide the confidence level of fault detections in each subregion by p-values, and then accurately localize the fault by comparing the p-values. Our experiments show that the proposed data-driven fault localization can greatly increase the accuracy of fault localization for distribution systems with high integration of DERs.

Original languageEnglish (US)
Title of host publicationiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference
Subtitle of host publicationGrid Modernization for Energy Revolution, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1021-1026
Number of pages6
ISBN (Electronic)9781728149301
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019 - Beijing, China
Duration: Nov 21 2019Nov 23 2019

Publication series

NameiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings

Conference

Conference2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019
CountryChina
CityBeijing
Period11/21/1911/23/19

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by Shenzhen Fundamental Research Fund under Grant No. JCYJ20170411102217994 and ZDSYS201707251409055, Shenzhen Peacock Plan under Grant KQTD2015033114415450 and Guangdong province “The Pearl River Talent Recruitment Program Innovative and Entrepreneurial Teams in 2017” – Data Driven Evolution of Future Intelligent Network Team under grant No. 2017ZT07X152.

Keywords

  • Distributed Energy Resources (DERs)
  • Support Vector Data Description (SVDD)
  • distribution systems
  • fault localization
  • probabilistic fault detection

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