Constrained Kalman filter based detection and isolation of sensor faults in a wind turbine

Nabil, Essam; Sobaih, Abdel-Azem; Abou-Zalam, Belal

doi:10.1109/icces.2015.7393020

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In [28], the authors have proposed a method to estimate the gearbox efficiency in the wind turbines. e idea is to detect the drop in the efficiency and generate a suitable residual to indicate the fault occurrence (see also [29,30]).…”

Section: Fault Detection and Tolerant Control In Wind Turbinesmentioning

confidence: 99%

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Optimal Fault Tolerant Control of Large-Scale Wind Turbines in the Case of the Pitch Actuator Partial Faults

Maati

Bahir

2020

Complexity

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In this paper, an adaptive fault tolerant control strategy is proposed to deal with the three pitch actuator faults in the large-scale wind turbines. Firstly, a simultaneous state and fault estimation was performed through a suitable LMI (linear matrix inequality) based optimal strategy. Hereafter, the new control law is designed using the previously estimated fault information. The actuator efficiency estimator uses as design parameters, respectively, the performance index γ against the wind and the learning rate Ξ. of the fault estimation algorithm. The study shows that the choice of the previous two parameters impacts the response time of the fault estimation and the correlation of the tracking error with the wind. The aim is to choose a small fault estimation response time while keeping a weak correlation between the tracking error and the wind turbulence noise. Finally, a tuning strategy is elaborated to choose the suitable γ and Ξ to match the reconfiguration objective.

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Section: Fault Detection and Tolerant Control In Wind Turbinesmentioning

confidence: 99%

“…(i) Equation(29) is solved by equating the identical powers on both sides. is allows extracting the value of the control matrix K.…”

mentioning

confidence: 99%

Optimal Fault Tolerant Control of Large-Scale Wind Turbines in the Case of the Pitch Actuator Partial Faults

Maati

Bahir

2020

Complexity

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“…Up to now, numerous fault detection techniques for wind turbine sensors have been proposed and discussed. On the one hand, physical-model based approaches have been proved to be effective and commonly adopted, typically including constrained Kalman filter [ 5 ], Takagi–Sugeno fuzzy model [ 6 ], and observed-based approaches [ 7 , 8 ]. Nevertheless, in practical engineering, it is unrealistic to establish a detailed mathematical model due to the complex electromechanical system construction and highly dynamic operating condition of wind turbines, which limits the further development and application of physical-model based methods, to a large extent.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Wang

Jiang

et al. 2020

Sensors

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Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.

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A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines

Alizadeh

Meskin

Khorasani

2018

IEEE Trans. Ind. Inf.

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Constrained Kalman filter based detection and isolation of sensor faults in a wind turbine

Cited by 4 publications

References 11 publications

Optimal Fault Tolerant Control of Large-Scale Wind Turbines in the Case of the Pitch Actuator Partial Faults

Optimal Fault Tolerant Control of Large-Scale Wind Turbines in the Case of the Pitch Actuator Partial Faults

A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines

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