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

Swetha Rani Kasimalla, Kuchan Park, Junho Hong, Young-Jin Kim, HyoJong Lee

TL;DR

This work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch within Power Electronics-Driven Grids (PEDGs) to enhance grid resilience.

Abstract

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.

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

TL;DR

This work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch within Power Electronics-Driven Grids (PEDGs) to enhance grid resilience.

Abstract

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.

Paper Structure

This paper contains 15 sections, 4 equations, 2 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Framework and Fault Detection Algorithm
  3. Development of Microgrid Framework
  4. Data Driven Approaches for the Type of Fault Identification and Localization
  5. Internal Fault Implementation
  6. Anomaly Implementation
  7. Data Driven Models
  8. Fault Detection Strategy
  9. Simulation Results and Discussion
  10. Single Switch Without Anomalies
  11. Single Switch With Anomalies
  12. Multiple Switches Without Anomalies
  13. Multiple Switches With Anomalies
  14. Conclusion
  15. Acknowledgment

Figures (2)

  • Figure 1: Multi-Layer Microgrid Framework: Integrating Fault Detection and Anomaly Identification
  • Figure : FaultNet-ML for Fault Detection and Classification