Artificial intelligence-based detection and mitigation of cyber

We present a novel two-tiered strategy employing data-driven and artificial intelligence methodologies. This approach is designed to detect and mitigate False-Data Injection (FDI) attacks,

Developments, challenges and future opportunities in cybersecure

Methods for attack detection and mitigation are identified and categorized based on microgrid modelling approaches and control objectives. Finally, emerging defence technologies and

Fault Detection, Classification, and Diagnostic in Microgrid Systems

Algorithms, approaches, and theories of data mining to solve the microgrid and smart-grid security problems. Manuscript Deadline: May 29, 2024. Please review the Manuscript

Enhanced cybersecurity and cyber-attack detection in smart DC micro

The combination of blockchain and wavelet transform methodologies can provide a robust, multi-layered approach to cyber-security in smart DC microgrids, ensuring both real-time detection

Anomaly Detection in a Smart Microgrid System

The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine

Securing smart microgrids with a novel multi-layer

A novel multi-layer cybersecurity framework that combines blockchain authentication, AI-driven anomaly detection, and system self-healing for smart microgrids. Real-time threat mitigation

Fault Detection and Fault Location in a Grid‐Connected Microgrid

ABSTRACT The significant prevalence of distributed energy resources in microgrids due to their unique characteristics and activities creates protection issues. This paper introduces fault

Detection of Microgrid Cyberattacks Using Network and System

To investigate the effectiveness of the NSM security monitoring platform, evaluate the proposed detection schemes, and observe the impact of cyberattacks on the microgrid operation, several