Hierarchical Data-Driven Protection for Microgrid with 100% Renewable Penetration: Preprint

Ahmed Zamzam, Jing Wang

Research output: Contribution to conferencePaper


The accurate detection and isolation of faults is critical for the reliable operation of microgrids (MGs). Traditional protection approaches are even more challenged for 100% renewable MGs because inverter-based resources (IBRs) are the only sources for fault current which are usually low and unpredictable/non-uniform. This calls for new protection scheme that can identify IBR fault responses and detect faults in MGs. Data-driven based protection can learn the pattern of IBR fault responses and make the correct decision to identify faults. Therefore, this paper presents a data-driven approach for fault localization in island MGs. The approach builds a training dataset of comprehensive fault scenarios that can be used to learn fault characteristics from processed measurements. The localization task is modeled as a binary classification problem at each relay, which simplifies the learning process. Then, a hierarchical decision mechanism is used to identify the fault location. The proposed approach is assessed using an exemplary MG with several grid-forming (GFM) and grid-following (GFL) inverters, where accurate estimation of fault location is achieved. The data-driven based protection approach developed in this paper provides a generic framework and useful guidance for power system protection engineers to achieve reliable protection for MGs with 100% renewables.
Original languageAmerican English
Number of pages11
StatePublished - 2023
EventIEEE Energy Conversion Congress and Expo - Nashville, TN
Duration: 29 Oct 20232 Nov 2023


ConferenceIEEE Energy Conversion Congress and Expo
CityNashville, TN

NREL Publication Number

  • NREL/CP-5D00-84518


  • artificial intelligence
  • microgrid
  • protection
  • relaying
  • renewable integration


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