Abstract
Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.
Original language | American English |
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Number of pages | 8 |
State | Published - 2024 |
Event | 2024 PES General Meeting - Seattle, WA Duration: 21 Jul 2024 → 25 Jul 2024 |
Conference
Conference | 2024 PES General Meeting |
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City | Seattle, WA |
Period | 21/07/24 → 25/07/24 |
NREL Publication Number
- NREL/CP-5D00-88949
Keywords
- decentralized algorithm
- fault localization
- microgrid protection