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Fault Diagnosis for Wind Turbine Sys...

Rahimilarki, Reihane.

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Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques./
Author:	Rahimilarki, Reihane.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	144 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28744987
ISBN:	9798535579221

Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques.
Rahimilarki, Reihane.

Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 144 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Northumbria at Newcastle (United Kingdom), 2021.

This item must not be sold to any third party vendors.

Concerning the fact that the number of wind turbines is increasing worldwide, it seems necessary to implement monitoring systems. To respond to this demand, this PhD thesis studies different fault diagnosis techniques in order to improve the reliability and reduce maintenance costs. Based on the fact that a considerable amount of data is stored via SCADA in every industry nowadays, the methods developed on historical data (called data-driven methods) can be very beneficial.By analyzing the historical data, the changing trends of a nonlinear dynamics, such as a wind turbine, can be predicted. Moreover, by applying suitable approaches, one can distinguish different faults based on the output of the system.The first part in this research reviews a neural network identification method by decoupling linear and nonlinear parts of a wind turbine model. As for the linear part, a Luenberger observer is designed, while for the nonlinear part, a neural network observer is proposed. By having an identification model for a wind turbine system, residual-based fault detection is studied.The second part in this research proposes a novel neuro-robust fault estimation method to deal with the occurred faults on actuators or sensors. The challenge in this method is environmental disturbances and sensor noises. To overcome these problems and simultaneously estimate the faults and the states, an augmented system is proposed in different scenarios of actuator faults or sensor faults. Then, a neural network updating rule is calculated along with the robust performance index to fully achieve this goal. The stability of the augmented system is guaranteed by having a Lyapunov function and input-to-state stability criteria.The third and final part in this research studies different structures of Convolutional Neural Networks for the problem of fault classification in a wind turbine.As working with time-series signals is challenging in deep learning classification, a pre-processing analysis is applied to prepare the data of system outputs for the input of the model.Each proposed method is applied to a 4.8 MW wind turbine benchmark and obtained results are illustrated and discussed to validate the accuracy and performance of the approach.

ISBN: 9798535579221Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Wind turbine

Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques.
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520 $a Concerning the fact that the number of wind turbines is increasing worldwide, it seems necessary to implement monitoring systems. To respond to this demand, this PhD thesis studies different fault diagnosis techniques in order to improve the reliability and reduce maintenance costs. Based on the fact that a considerable amount of data is stored via SCADA in every industry nowadays, the methods developed on historical data (called data-driven methods) can be very beneficial.By analyzing the historical data, the changing trends of a nonlinear dynamics, such as a wind turbine, can be predicted. Moreover, by applying suitable approaches, one can distinguish different faults based on the output of the system.The first part in this research reviews a neural network identification method by decoupling linear and nonlinear parts of a wind turbine model. As for the linear part, a Luenberger observer is designed, while for the nonlinear part, a neural network observer is proposed. By having an identification model for a wind turbine system, residual-based fault detection is studied.The second part in this research proposes a novel neuro-robust fault estimation method to deal with the occurred faults on actuators or sensors. The challenge in this method is environmental disturbances and sensor noises. To overcome these problems and simultaneously estimate the faults and the states, an augmented system is proposed in different scenarios of actuator faults or sensor faults. Then, a neural network updating rule is calculated along with the robust performance index to fully achieve this goal. The stability of the augmented system is guaranteed by having a Lyapunov function and input-to-state stability criteria.The third and final part in this research studies different structures of Convolutional Neural Networks for the problem of fault classification in a wind turbine.As working with time-series signals is challenging in deep learning classification, a pre-processing analysis is applied to prepare the data of system outputs for the input of the model.Each proposed method is applied to a 4.8 MW wind turbine benchmark and obtained results are illustrated and discussed to validate the accuracy and performance of the approach.
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