Operation Security Prediction for Wind Turbines Using Convolutional Neural Networks: A Proposed Method-Reference-Cited by-同舟云学术

Operation Security Prediction for Wind Turbines Using Convolutional Neural Networks: A Proposed Method

Published:2023-01 Issue:1 Volume:9 Page:4-9
ISSN:2333-942X
Container-title:IEEE Systems, Man, and Cybernetics Magazine
language:
Short-container-title:IEEE Syst. Man Cybern. Mag.

Author:

Hong Sheng¹^ORCID,Feng Tao²,Hu Jun³^ORCID,Zhang Xiao⁴

Affiliation:

1. Professor, Nanchang University, Nanchang., JiangXi, China

2. Senior Engineer, Duchengweiye Group Co. Ltd., Beijing, China

3. Senior Engineer of Informatization Systems, Huaneng Power International Inc., Huaiyin Power Plant, Huaian, Jiangsu, China

4. School of Cyber Science and Technology, Beihang University, Beijing, China

Funder

National Key Research and Development Program of China

Beijing Natural Science Fund-Haidian Original Innovation Joint Fund

Industrial Internet Innovation and Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Human Factors and Ergonomics,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6745853/10014542/10014609.pdf?arnumber=10014609

Reference18 articles.

1. Research on fault diagnosis algorithm of flight control system based on neural network sliding mode observer;quan;J Guizhou Univ (Natural Sci Ed ),2019

2. ISSUES ON INTEGRATION OF FAULT DIAGNOSIS AND RECONFIGURABLE CONTROL IN ACTIVE FAULT-TOLERANT CONTROL SYSTEMS

3. Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

4. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

5. Bibliographical review on reconfigurable fault-tolerant control systems