Fault tolerant control of wind turbines: a benchmark model
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TLDR
A benchmark model for simulation of fault detection and accommodation schemes of the wind turbine on a system level containing sensors, actuators and systems faults in the pitch system, drive train, generator and converter system is presented.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2009-01-01 and is currently open access. It has received 272 citations till now. The article focuses on the topics: Wind power & Turbine.read more
Citations
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Journal ArticleDOI
Fault-Tolerant Control of Wind Turbines: A Benchmark Model
TL;DR: In this paper, the authors presented a test benchmark model for the evaluation of fault detection and accommodation schemes for a wind turbine on a system level, and it includes sensor, actuator, and system faults, namely faults in the pitch system, the drive train, the generator, and the converter system.
Journal ArticleDOI
A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
TL;DR: An efficient machine learning method, random forests in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework that is robust to various wind turbine models including offshore ones in different working conditions.
Journal ArticleDOI
Data-driven design of robust fault detection system for wind turbines
TL;DR: In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark, where robust residual generators directly constructed from available process data are used to achieve the robustness of the residual signals related to the disturbances.
Fault Tolerant Control of Wind Turbines – a Benchmark Model
TL;DR: This benchmark model deals with the wind turbine on a system level, and it includes sensor, actuator, and system faults, namely faults in the pitch system, the drive train, the generator, and the converter system.
Journal ArticleDOI
Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information
TL;DR: A new fault detector based on a recently developed unsupervised learning method, denoising autoencoder (DAE), which offers the learning of robust nonlinear representations from data against noise and input fluctuation is proposed.
References
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Book
Wind Energy Handbook
TL;DR: The Wind Energy Handbook as discussed by the authors provides an overview of wind turbine technology and wind farm design and development, as well as a survey of alternative machine architectures and an introduction to the design of the key components.
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Condition monitoring and fault detection of wind turbines and related algorithms: A review
TL;DR: In this article, the authors reviewed different techniques, methodologies and algorithms developed to monitor the performance of wind turbine as well as for an early fault detection to keep away the wind turbines from catastrophic conditions due to sudden breakdowns.
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Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture
TL;DR: The question of theoretical stability of the torque controller is addressed, showing that the rotor speed is asymptotically stable under the torque control law in the constant wind speed input case and L/sub 2/ stable with respect to time-varying wind input.
Standard and adaptive techniques for maximizing energy capture
TL;DR: In this article, the authors used the Controls Advanced Research Turbine (CART) as a model for this article's research, which is located in Golden, Colorado, at the U.S. National Renewable Energy Laboratory's National Wind Technology Center.
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Sensor Fault Diagnosis of Wind Turbines for Fault Tolerant
TL;DR: In this article, a wind turbine model is built based on the closed loop identification technique, where the wind dynamics is included in the model, and the fault detection issue is investigated based on residual generated by Kalman filter.