Fault section diagnosis (FSD) is essential for ensuring the effective operation of power
systems. To determine the faulty sections accurately, we proposed an improved binary …

The complex interactions between the weather, the environment, and electrical infrastructure
that result in power outages are not fully understood, but because of the threat of climate …

Foundation models (FMs) currently dominate news headlines. They employ advanced deep
learning architectures to extract structural information autonomously from vast datasets …

The unknow fault diagnosis technology in industrial systems implies significant engineering
application value and opportunities. The difficulty stems from the fact that the unknown fault …

Deep neural networks (DNNs) are often coupled with physics-based and data-driven
models to perform fault detection and health monitoring. The system models serve as digital …

A large volume of data is being captured through the Phasor Measurement Unit (PMU),
which opens new opportunities and challenges to the study of transmission line faults. To be …

Deep learning-based object detection is a powerful approach for detecting faulty insulators
in power lines. This involves training an object detection model from scratch, or fine tuning a …

Transmission lines are a vital component of the power system, and their operational status
directly affects the safety and stability of the entire grid. This study utilizes the association …

Deep neural networks (DNNs) are often coupled with physics-based models or data-driven
surrogate models to perform fault detection and health monitoring of systems in the low data …

Flashed over insulator faults are the most significant faults in high voltage line insulators.
They are complicated to identify using traditional methods due to their labor-intensive …