An Anomaly Detection Approach Based on Machine Learning and SCADA Data for Condition Monitoring of Wind Turbines-Reference-Cited by-同舟云学术

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An Anomaly Detection Approach Based on Machine Learning and SCADA Data for Condition Monitoring of Wind Turbines

Published:2018-06 Issue: Volume: Page:
ISSN:
Container-title:2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
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Author:

Cui Yue,Bangalore Pramod,Tjernberg Lina Bertling

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8422007/8440201/08440525.pdf?arnumber=8440525

Cited by 20 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. Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms;Energies;2024-02-20

2. Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders;IEEE Transactions on Industrial Informatics;2024-01

3. Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms;Wind Energy Science;2023-06-05

4. A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis;International Journal of Green Energy;2023-05-29

5. Anomaly detection for parabolic trough power plants with density-based outlierness;THE INTERNATIONAL CONFERENCE ON BATTERY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLES (ICB-REV) 2022;2023

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