Identifying early defects of wind turbine based on SCADA data and dynamical network marker

Author:

Ruiming Fang,Minling Wu,xinhua Guo,Rongyan Shang,Pengfei Shao

Funder

National Natural Science Foundation of People’s Republic of China

Natural Science Foundation of Fujian Province China

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Cited by 25 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. A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions;Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023);2024-04-01

2. Re-Stabilizing Large-Scale Network Systems Using High-Dimension Low-Sample-Size Data Analysis;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

3. Correlation analysis based relevant variable selection for wind turbine condition monitoring and fault diagnosis;Sustainable Energy Technologies and Assessments;2023-12

4. A Method for Monitoring the Operation Status of Wind Turbines Based on Cloud Services;2023 International Conference on Electronics and Devices, Computational Science (ICEDCS);2023-09-22

5. Fault detection of wind turbines using SCADA data and genetic algorithm-based ensemble learning;Engineering Failure Analysis;2023-06

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