The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location
When the operating condition of MG changes, the protection system must identify it, and the way it determines the changes is of crucial importance for the safe and reliable operation of MG.
The traditional methods for detection of faults in microgrid have faced significant challenges like inability to handle various fault scenarios. Therefore, this research proposes modified dragonfly
To overcome existing limitations, this paper presents a new intelligent fault detection method for microgrids, utilizing advanced signal processing techniques such as MVMD for feature
Abstract— Fault diagnosis is essential to attain the microgrid''s stable, reliable, and economical operation. However, a microgrid''s fault current varies drastically based on the connected
The effectiveness of the proposed methods is examined under seven distinct fault types, each featuring varying fault resistance, in a 100% inverter-based microgrid consisting of four inverters.
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
This critical study provides valuable information for researchers and professionals aiming to refine fault detection and isolation methods and improve the efficiency of DC microgrid systems.
We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify...
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