A fault prediction framework for Doubly‐fed induction generator under time‐varying operating conditions driven by digital twin
Author:
Affiliation:
1. College of Intelligent Manufacturing Modern Industry Xinjiang University Urumqi Xinjiang China
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/elp2.12280
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1. Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings
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4. Impact of Actual Wind Speed Distribution on the Fault Characteristic of DFIG Rotor Winding Asymmetry
5. Detection of rotor electrical asymmetry in wind turbine doubly‐fed induction generators
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1. Digital twin-driven prognostics and health management for industrial assets;Scientific Reports;2024-06-11
2. Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine;Energies;2024-04-19
3. Design and Implementation of Digital Twin Diesel Generator Systems;Energies;2023-09-05
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