One-class classifier based fault detection 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

3 Scopus citations

Abstract

The integration of distributed energy resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flows. Conventional protection schemes are based upon local measurements and simple linear system models, thus they cannot handle the new complexity and power flow patterns in systems with high DERs penetration. In this paper, we propose a data-driven protection framework to address the challenges induced by DERs. Considering the limited available data under fault conditions, we adopt the support vector data description (SVDD) method, a commonly used one-class classifier, for distribution system fault detection. The proposed method is tested under the IEEE 123-node test feeder and simulation results show that our proposed SVDD-based fault detection method significantly improves the robustness and resilience against DERs in comparison with conventional protection systems.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages932-936
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Externally publishedYes
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period11/26/1811/29/18

Bibliographical note

Funding Information:
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)
  • Distribution systems
  • Fault detection
  • Support vector data description (SVDD)

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