Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data-Reference-Cited by-同舟云学术

Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data

Published:2023-12 Issue:6 Volume:149 Page:
ISSN:0733-9402
Container-title:Journal of Energy Engineering
language:en
Short-container-title:J. Energy Eng.

Author:

Feng Chenlong¹,Liu Chao²^ORCID,Jiang Dongxiang³,Kong Detong⁴,Zhang Wei⁴

Affiliation:

1. Master’s Student, Dept. of Energy and Power Engineering, Tsinghua Univ., Beijing 100084, PR China.

2. Associate Professor, Dept. of Energy and Power Engineering, Tsinghua Univ., Beijing 100084, PR China; Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua Univ., Beijing 100084, PR China (corresponding author). ORCID: .

3. Professor, Dept. of Energy and Power Engineering, Tsinghua Univ., Beijing 100084, PR China; State Key Laboratory of Control and Simulation of Power System and Generation Equipment, Tsinghua Univ., Beijing 100084, PR China.

4. Director, Huadian Electric Power Research Institute Co., Ltd., No.10, Xiyuanyi Rd., Xihu Science Park, Xihu District, Hangzhou 310030, PR China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JLEED9.EYENG-4843

Reference39 articles.

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4. Bearing temperature monitoring of a Wind Turbine using physics-based model

5. A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines

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