A Novel Adaptive Parallel Model with Knowledge-Aided Conversion Efficiency Assessment for Wind Turbine Condition Monitoring-Reference-Cited by-同舟云学术

A Novel Adaptive Parallel Model with Knowledge-Aided Conversion Efficiency Assessment for Wind Turbine Condition Monitoring

Published:2023-08 Issue:4 Volume:149 Page:
ISSN:0733-9402
Container-title:Journal of Energy Engineering
language:en
Short-container-title:J. Energy Eng.

Author:

Jing Hua¹^ORCID,Zhao Chunhui²^ORCID

Affiliation:

1. Doctoral Student, College of Control Science and Engineering, Zhejiang Univ., Hangzhou 310027, China. ORCID: .

2. Professor, College of Control Science and Engineering, Zhejiang Univ., Hangzhou 310027, China (corresponding author). ORCID: .

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-4850

Reference38 articles.

1. Aerodynamic characteristics of variously modified leading-edge protuberanced (LEP) wind turbine blades under various turbulent intensities

2. OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes

3. Bui, T. D., C. Nguyen, and R. E. Turner. 2017a. “Streaming sparse Gaussian process approximations.” In Advances in neural information processing systems, 3299–3307. New York: Curran Associates.

4. A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation;Bui T. D.;J. Mach. Learn. Res.,2017

5. Wind turbine power coefficient models based on neural networks and polynomial fitting

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. Semi-Supervised Domain Adaptation with Feature Separation for Wind Turbine Anomaly Detection;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17