A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers
Author:
Affiliation:
1. NIT, Silchar,E & I Department,Silchar,India
2. IIIT, Bhubaneswar,ETC Department,Bhubaneswar,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9797885/9797929/09798026.pdf?arnumber=9798026
Reference11 articles.
1. Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study
2. A comprehensive analysis of blade tip for vertical axis wind turbine: Aerodynamics and the tip loss effect
3. Validation of kinematic wind turbine wake models in complex terrain using actual windfarm production data
4. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks
5. Actor-critic continuous state reinforcement learning for wind-turbine control robust optimization
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study;2023 IEEE Region 10 Symposium (TENSYMP);2023-09-06
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