Hierarchical Data-Driven Protection for Microgrid with 100% Renewable Penetration: Preprint
Conference
·
OSTI ID:2205311
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.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC36-08GO28308;
- OSTI ID:
- 2205311
- Report Number(s):
- NREL/CP-5D00-84518; MainId:85291; UUID:754b2a29-9455-4723-936e-057afb6e1278; MainAdminID:70114
- Conference Information:
- Presented at the IEEE Energy Conversion Congress and Exposition (ECCE), 29 October - 2 November 2023, Nashville, Tennessee
- Country of Publication:
- United States
- Language:
- English
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