Switch Dual-line Aggregation Fault Detection

A novel fault diagnosis model based on deep feature

To address these challenges, we propose a novel fault diagnosis model based on a deep feature fusion network (DFFN) specifically designed for

Optimizing a Digital Twin for Fault Diagnosis in Grid Connected

Abstract—In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters.

Graph Neural Networks for Fault Diagnosis in

The proposed model uses multiple GNN layers as the backbone network and a multitask header to handle multiple fault diagnosis tasks

Graph Neural Networks for Fault Diagnosis in Photovoltaic

The proposed model uses multiple GNN layers as the backbone network and a multitask header to handle multiple fault diagnosis tasks simultaneously, where the subtask network includes

A novel fault diagnosis model based on deep feature fusion network

To address these challenges, we propose a novel fault diagnosis model based on a deep feature fusion network (DFFN) specifically designed for railway dual-switch machines in traction...

Fault Intelligence: Distribution Grid Fault Detection and Classification

This document describes and classifies common (and some not so common) fault types, along with characteristics, and analytics data requirements, so that we can identify opportunities for synergy

Fault diagnosis of wind turbine based on dual-channel feature

In order to extract discriminative features from vibration signals under variable speed operating conditions, a dual-channel feature aggregation network (DCNet) with attention mechanism

Advanced Fault Diagnosis in Power Electronics: Switch Open Faults

This research addresses the evolving challenges in fault diagnosis and contributes to advancing the field of power electronics by providing a reliable diagnostic solution for modern systems.

Fault detection in electrical power systems using attention-GRU-based

To overcome these shortcomings, this paper puts forward an Attention-GRU-Based Fault Classifier (AGFC-Net), which employs a sophisticated attention mechanism for improved feature

Fault Indicators and Sensors

Quickly identify faulted line segments and enable advanced protection solutions by deploying fault indicators and sensors on feeder lines, at overhead-to-underground transitions, and in pad-mounted

Continual learning fault diagnosis: A dual-branch adaptive aggregation

A new intelligent fault diagnosis method, CLFD, is proposed in this paper for fault diagnosis with machine increments arising in the operation of mechanical systems under the

Salient Feature and Dual-Attention-Based Method for Fault-Cause

This method provides a practical and scalable solution for fault-cause identification in real-world power transmission systems, with the potential to enhance grid stability and improve the overall efficiency of

Prospettiva Cyber Systems PCS