1034 / 2019-05-18 03:49:19
Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources
distributed energy resources,distribution systems,fault localization,probabilistic fault detection,support vector data description
Draft Accepted
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 multi-level system regionalization and probabilistic fault detections on all the sub-regions. 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 sub-region by the 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.
Important Date
  • Conference Date

    Oct 21

    2019

    to

    Oct 24

    2019

  • Oct 13 2019

    Abstract Notification of Acceptance

  • Oct 13 2019

    Draft paper submission deadline

  • Oct 14 2019

    Draft Paper Acceptance Notification

  • Oct 24 2019

    Registration deadline

  • Oct 29 2019

    Final Paper Deadline

Organized By
Xi'an Jiaotong University
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