To address these complexities, this paper proposes a novel topology-aware fault diagnosis approach that integrates Message Passing Neural Networks (MPNNs) with a Graph-Lasso
Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available...
This paper presents a method for detecting faults in a micro grid using Artificial intelligence (AI). As we know fault detection is very important for microgrid
Several studies have explored fault detection and classification methods for microgrids. These methods can be categorized into various approaches [4, 8]. A significant portion of these
A robust protection scheme is presented for fault detection, classification, and location identification in an islanded DC microgrid comprising PV arrays, wind turbines, battery energy storage system, and
Various fault types, with varying parameters are simulated to validate the proposed approach. The results indicate that the proposed methodology is capable of recognizing, classifying,
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 ensure the delivery of reliable and high-quality energy to end consumers while alleviating stress on the utility grid, this paper introduces a novel methodology for the efficient
In the case of a line-to-ground fault within the DC microgrid, the current from PV and PMSG based WECS to the fault becomes negligible, posing challenges in fault detection for such occurrences.
In this context, the application of machine learning techniques has shown promise in enhancing the accuracy of fault detection and classification in MGs. A critical component of this
PDF version includes complete article with source references. Suitable for printing and offline reading.
