Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy-Reference-Cited by-同舟云学术

Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy

Published:2022-05 Issue: Volume:313 Page:118882
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Sun Shilin,Wang Tianyang,Yang Hongxing,Chu Fulei

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference45 articles.

1. Nacelle and tower effect on a stand-along wind turbine energy output—A discussion on field measurements of a small wind turbine;Guo;Appl Energy,2021

2. A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes;He;Appl Energy,2021

3. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks;Xiang;Appl Energy,2022

4. A mathematical approach to minimizing the cost of energy for large utility wind turbines;Chen;Appl Energy,2018

5. Vibration-induced aerodynamic loads on large horizontal axis wind turbine blades;Liu;Appl Energy,2017

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