Deep Learning-Enabled System Diagnosis in Microgrids: A Feature
The proposed framework is validated on a simulated microgrid environment, illustrating robust performance in detecting and classifying both physical and cyber-related disturbances in
The proposed framework is validated on a simulated microgrid environment, illustrating robust performance in detecting and classifying both physical and cyber-related disturbances in
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
Therefore, a converter-based microgrid platform has been developed on the existing HTB to implement and test the proposed fault detection method. Additionally, the HTB has been
A comprehensive end-to-end microgrid protection solution that offers a range of functionalities—from data collection to fault detection, localization, and isolation.
The platform serves as a foundation for next-generation microgrid control systems that demand real-time intelligence, scalability, and reliability across evolving smart grid landscapes.
This paper proposes an intelligent diagnosis framework of microgrid based on cloud–edge integration. First, the digital twin model of the microgrid is established on the cloud server. Based on
This paper introduces fault detection and its location in an MG. The aim of the investigation is to enhance the system''s efficiency and dependability, and fault detection and
The proposed protection scheme has been tested and applied to the configuration of the microgrid using the Matlab/Simulink platform and has been demonstrated to be an effective means to
According to the fault characteristics and the ring structure of DC microgrids, this paper proposes a rapid detection scheme based on the differential current and current derivative without de
Leveraging the recent strides in artificial intelligence, this paper introduces a novel multi-agent-based protection scheme for DC microgrids.
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