skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Real-time monitoring, prognosis, and resilient control for wind turbine systems

Abstract

This special issue aims to provide a platform for academic and industrial communities to report recent results and emerging research in real-time monitoring, fault diagnosis, prognosis, and resilient control and design of wind turbine systems. After a strict peer-review process, 20 papers were selected, which represent the most recent progress of the real-time monitoring, diagnosis, prognosis, and resilient control methods/techniques in wind turbine systems.

Authors:
 [1];  [2]
  1. Univ. of Northumbria, Newcastle upon Tyne (United Kingdom)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1417288
Alternate Identifier(s):
OSTI ID: 1549386
Report Number(s):
NREL/JA-5000-70021
Journal ID: ISSN 0960-1481
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Renewable Energy
Additional Journal Information:
Journal Volume: 116; Journal Issue: PB; Journal ID: ISSN 0960-1481
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 42 ENGINEERING; wind turbine; wind energy; monitoring; prognosis; resilient control; analysis; fault detection

Citation Formats

Gao, Zhiwei, and Sheng, Shuangwen. Real-time monitoring, prognosis, and resilient control for wind turbine systems. United States: N. p., 2017. Web. doi:10.1016/j.renene.2017.10.059.
Gao, Zhiwei, & Sheng, Shuangwen. Real-time monitoring, prognosis, and resilient control for wind turbine systems. United States. https://doi.org/10.1016/j.renene.2017.10.059
Gao, Zhiwei, and Sheng, Shuangwen. 2017. "Real-time monitoring, prognosis, and resilient control for wind turbine systems". United States. https://doi.org/10.1016/j.renene.2017.10.059. https://www.osti.gov/servlets/purl/1417288.
@article{osti_1417288,
title = {Real-time monitoring, prognosis, and resilient control for wind turbine systems},
author = {Gao, Zhiwei and Sheng, Shuangwen},
abstractNote = {This special issue aims to provide a platform for academic and industrial communities to report recent results and emerging research in real-time monitoring, fault diagnosis, prognosis, and resilient control and design of wind turbine systems. After a strict peer-review process, 20 papers were selected, which represent the most recent progress of the real-time monitoring, diagnosis, prognosis, and resilient control methods/techniques in wind turbine systems.},
doi = {10.1016/j.renene.2017.10.059},
url = {https://www.osti.gov/biblio/1417288}, journal = {Renewable Energy},
issn = {0960-1481},
number = PB,
volume = 116,
place = {United States},
year = {Sat Oct 21 00:00:00 EDT 2017},
month = {Sat Oct 21 00:00:00 EDT 2017}
}

Journal Article:

Citation Metrics:
Cited by: 41 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Real-time fault diagnosis and fault-tolerant control
journal, June 2015


Condition monitoring and fault detection of wind turbines and related algorithms: A review
journal, January 2009


Condition monitoring of wind turbines: Techniques and methods
journal, October 2012


Current signature analysis to monitor DFIG wind turbine generators: A case study
journal, February 2018


Performance analysis of electrical signature analysis-based diagnostics using an electromechanical model of wind turbine
journal, February 2018


Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis
journal, February 2018


Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy
journal, February 2018


Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion
journal, February 2018


Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data
journal, February 2018


A testing procedure for wind turbine generators based on the power grid statistical model
journal, February 2018


Parameter-varying modelling and fault reconstruction for wind turbine systems
journal, February 2018


Hybrid method for remaining useful life prediction in wind turbine systems
journal, February 2018


Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data
journal, February 2018


Real-time power switch fault diagnosis and fault-tolerant operation in a DFIG-based wind energy system
journal, February 2018


Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation
journal, February 2018


Performance optimization of a wind turbine column for different incoming wind turbulence
journal, February 2018