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

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