Graph theory within power electronics, developed over a 50-year span, is continually
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …

In this paper, a simulation and analysis of 132 kV Substation in feeds western Iraq have
been presented including a short circuit (SC) analysis. This work helps to properly control …

This article investigates the ability of graph neural networks (GNNs) to identify risky
conditions in a power grid over the subsequent few hours, without explicit, high-resolution …

With the growth of factory automation, deep learning-based methods have become popular
diagnostic tools because they can extract features automatically and diagnose faults under …

Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early
diagnosis of cable faults is the most effective approach to prevent system downtime and …

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution
power system Traveling Wave (TW)-based protection schemes. Simulated faults on the IEEE …

Abstract The Ship Integrated Power System (SIPS) is evolving into a sophisticated network
with prediction and active control functions, so accurate localization and identification of …

This paper presents a novel fault detection and identification method for low-voltage direct
current (DC) microgrid with meshed configuration. The proposed method is based on graph …

This paper develops a non-intrusive load monitoring (NILM) method in future shipboard
power systems (SPS) using discrete wavelet transform-based convolutional neural networks …

Shipboard microgrids (SMGs) have played a significant role in the concept of all-electric
ships (AES), with increasing power demand from loads during operation. The system …