Enhancing Cybersecurity and Fault Detection in Smart Grids and Microgrids

The recent developments in the research area of smart grids and microgrids have shown a significant shift towards enhancing cybersecurity, improving fault detection, and leveraging data-driven approaches for better system resilience and efficiency. There is a growing emphasis on decentralized and local control strategies to mitigate the risks associated with centralized systems, particularly in the context of voltage regulation in DC microgrids and cyber-resilient control in distributed microgrids. Innovations in machine learning and data analytics are being integrated into various aspects of grid management, from detecting malicious activities in vehicle-to-grid systems to restoring cyber systems in digital substations. Additionally, the field is witnessing advancements in synthetic data generation tools for electric vehicle energy flexibility, which are crucial for optimizing demand response strategies. Battery degradation analysis using field data is also emerging as a key area, providing insights into real-world battery performance and longevity. Notably, the integration of artificial intelligence for fault detection in distributed energy resources is proving to be a game-changer, enhancing the reliability and safety of microgrid operations.

Noteworthy Papers:

  • A novel approach combining probabilistic and combinatorial group testing with machine learning predictions for detecting malicious users in vehicle-to-grid systems.
  • A decentralized method for bus voltage restoration in DC microgrids, enhancing reliability and reducing cybersecurity threats.
  • An AI-enhanced system for detecting and localizing inverter faults in microgrids, crucial for preventing grid failures.

Sources

Behavior-Aware Efficient Detection of Malicious EVs in V2G Systems

Decentralized Bus Voltage Restoration for DC Microgrids

A Novel Combined Data-Driven Approach for Electricity Theft Detection

FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging Energy Flexibility

Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations

SDN-Based Smart Cyber Switching (SCS) for Cyber Restoration of a Digital Substation

Data-Driven Graph Switching for Cyber-Resilient Control in Microgrids

Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data

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

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