DINGO: Digital assistant to grid operators for resilience management of power distribution system

Pannala, Sanjeev; Srivastava, Anurag K.; Kandaperumal, Gowtham; Sadanandan, Sajan K.

doi:10.1016/j.epsr.2022.108076

DINGO: Digital assistant to grid operators for resilience management of power distribution system

Journal Article · Mon May 23 00:00:00 EDT 2022 · Electric Power Systems Research

DOI:https://doi.org/10.1016/j.epsr.2022.108076· OSTI ID:1977112

Pannala, Sanjeev ^[1]; ^[2]; Kandaperumal, Gowtham ^[3]; ^[2]

Washington State University, Pullman, WA (United States); OSTI
West Virginia University, Morgantown, WV (United States)
ComEd-An Exelon Company, Oak Brook, IL (United States)

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

View Accepted Manuscript (DOE)

Research Organization:: Washington State University, Pullman, WA (United States)

Sponsoring Organization:: USDOE Office of International Affairs (IA)

Grant/Contract Number:: IA0000025

OSTI ID:: 1977112

Journal Information:: Electric Power Systems Research, Journal Name: Electric Power Systems Research Journal Issue: C Vol. 210; ISSN 0378-7796

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
NLP
data-driven modeling
distribution networks
outage detection
resiliency
restoration

DINGO: Digital assistant to grid operators for resilience management of power distribution system

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