AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed Energy Resources in Microgrids

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detec...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Kasimalla, Swetha Rani, Park, Kuchan, Hong, Junho, Young-Jin, Kim, Lee, HyoJong
Format: Article
Language:English
Subjects:
Anomalies
Artificial intelligence
Data mining
Distributed generation
Electric power grids
Electronics
Energy distribution
Energy sources
Fault detection
Faults
Inverters
Modernization
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Summary:The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment.
ISSN:2331-8422