Smart microgrid system detection - ARTEMISS SOLAR INFRA

Anomaly Detection in a Smart Microgrid System Using Cyber

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
Network security risk detection method for smart microgrid monitoring

In this paper, a network security risk detection method based on Artificial Immune Algorithm for the smart microgrid monitoring system is proposed, which has the advantages of high
Cyberattack Detection in Virtualized Microgrids Using LightGBM

The results confirm that lightweight machine-learning–based intrusion-detection methods can provide fast, accurate, and efficient cyberattack detection without relying on complex deep-learning models.
Detection of Microgrid Cyberattacks Using Network and System

The capabilities of this platform are demonstrated on a detailed microgrid model that is deployed on a real-time co-simulation testbed. A hybrid rule-based and machine learning anomaly detection
Fault Detection, Classification, and Diagnostic in Microgrid Systems

In this way, machine learning algorithms are deployed to analyze the data collected from a set of installed sensors in the microgrid system in order to detect anomalous events in the system.
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
Detection of disturbances and cyber-attacks in smart grids using

Ankitdeshpandey & Karthi, R. Development of intrusion detection system using deep learning for classifying attacks in power systems.
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