Fault Detection of Wind Turbine System Based on Deep Learning and System Identification
Author:
Affiliation:
1. University of Alberta,Department of Electrical and Computer Engineering,Edmonton,AB,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9894047/9894052/09894254.pdf?arnumber=9894254
Reference10 articles.
1. Introduction to Data-Driven Methodologies for Prognostics and Health Management
2. Condition monitoring of wind turbine for rotor fault detection under non stationary conditions;dahiya;Ain Shams Engineering Journal,2018
3. Fault-Tolerant Control of Wind Turbines: A Benchmark Model
4. Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model
5. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection
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1. Fault identification and classification of wind turbine blades based on improved DenseNet;2024 5th International Conference on Computer Engineering and Application (ICCEA);2024-04-12
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