Adaptive Protective Relay Settings - A Vision to the Future

Srijib Mukherjee, Mike Marshall, Travis Smith, Emilio Piesciorvosky, Isabelle Snyder, Chris Sticht

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Adaptive relaying utilizes the continuously changing status of the power system as the basis for online adjustment of the power system relay settings. Fundamentally they are protection schemes that adjust settings and/or logic of operations based on the prevailing conditions of the system. These adjustments can include, but are not limited to, the logging of data for post-mortem analysis, communication throughout the system, as well as changing relay parameters. Adaptive relaying considers the fact that the status of a power system can change. These include system configuration changes, load effect, cold load pickup, end-of-line protection, transformer protection, and automatic reclosing. In this research, the author focus on the need for a secure, selective, and reliable system for adaptive overcurrent protection in T&D and Distributed Energy Systems. Various types of adaptive methods are presented and explained along with some pros and cons of each.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE Rural Electric Power Conference, REPC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-30
Number of pages6
ISBN (Electronic)9781665481670
DOIs
StatePublished - 2022
Event2022 IEEE Rural Electric Power Conference, REPC 2022 - Savannah, Georgia
Duration: Apr 5 2022Apr 7 2022

Publication series

NamePapers Presented at the Annual Conference - Rural Electric Power Conference
Volume2022-April
ISSN (Print)0734-7464

Conference

Conference2022 IEEE Rural Electric Power Conference, REPC 2022
Country/TerritoryGeorgia
CitySavannah
Period04/5/2204/7/22

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retain s a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U.S. Department of Energy

    Keywords

    • Adaptive
    • artificial intelligence
    • group settings
    • high impedance
    • machine learning
    • model driven
    • self-setting

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