Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

Fernandez-Canti, Rosa M.; Blesa, Joaquim; Tornil-Sin, Sebastiàn; Puig, Vicenç

doi:10.1016/j.arcontrol.2015.08.002

Cited by 28 publications

(13 citation statements)

References 33 publications

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“…In [76], artificial neural network was used to estimate nonlinear term in wind turbine model, and linear matrix inequality optimization was addressed to find an optimal observer gain so that a robust actuator fault estimation was achieved for 4.8 MW wind turbine Benchmark system. In [77], fault detection and isolation for wind turbines were addressed by using a mixed Bayesian/Set-membership, where modeling errors were described as unknown but bounded perturbations from the viewpoint of set membership method, while measurement noises were characterized as bounded noises following a statistical distribution. The approaches in [76,77] are actually a hybrid of model-based and datadriven approaches.…”

Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

“…In [77], fault detection and isolation for wind turbines were addressed by using a mixed Bayesian/Set-membership, where modeling errors were described as unknown but bounded perturbations from the viewpoint of set membership method, while measurement noises were characterized as bounded noises following a statistical distribution. The approaches in [76,77] are actually a hybrid of model-based and datadriven approaches.…”

Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

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An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented.

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Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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“…A comprehensive literature review in Bayesian wind turbine FD can be found in . There, some other methods can be found that have been proposed more recently, such as Bayesian and non‐Bayesian based fault diagnosis approaches including non‐stationary stochastic embedding (NSSE), robust risk adjusted controllers and probabilistic fault detection Bayesian Markov chain Monte Carlo (MCMC).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

Habibi

Howard

Habibi

2018

Asian Journal of Control

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In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one‐step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated.

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“…Mode-matched filter: For each robust estimator, the estimated meanx j (k + 1) is calculated under the current system condition w(k) = j. According to the calculation process of a single robust estimator in Section 3.2, the gain L j (w(k)) can be given by (27); the robust estimator (REs) can be given by (20), and the robust estimator residuals r j (k + 1), weighting matrix T j (w(k + 1)), and covariance matrix S j (w(k + 1)) will be updated in a similar way.…”

Section: Stochastic Hybrid Estimation Algorithmmentioning

confidence: 99%

“…A lot of research has been started in recent years for the fault diagnosis and isolation of wind turbines [22][23][24][25][26][27][28][29]. Karim [22] designed an observer scheme for FDI, which is integrated with a maximum-shift strategy and a time-varying Kalman filter for the additive and multiplicative measurement failures of voltage and current.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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Abstract:In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators' faults of the WECS. Both the system status and the actuators' faults can be detected by the FDI method and it can effectively quantify the actuators' fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

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Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

Cited by 28 publications

References 33 publications

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

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