Wind Turbine Fault Detection Using Counter-Based Residual Thresholding

Ozdemir, Ahmet Arda; Seiler, Peter; Balas, Gary J.

doi:10.3182/20110828-6-it-1002.01758

Cited by 61 publications

(46 citation statements)

References 8 publications

(10 reference statements)

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“…This benchmark has been used in an international competition on FDI and FTC in Wind Turbines aiming at finding the best schemes to diagnose and handle the different faults proposed. According to [12,13,14], the most promising solutions proposed are: Data-driven [15], Gaussian Kernel Support Vector Machine [16], Estimation [17], Up-Down Counters [18], Combined Observer and Kalman Filter [19], General Fault Model [20], Fuzzy Models [21], online identification [22] and Set-Membership/Virtual Sensors-Actuators [23,24].…”

Section: Introductionmentioning

confidence: 99%

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

Fernandez-Canti¹,

Blesa

Tornil-Sin³

et al. 2015

Annual Reviews in Control

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This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Setmembership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.

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Section: Introductionmentioning

confidence: 99%

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

Fernandez-Canti¹,

Blesa

Tornil-Sin³

et al. 2015

Annual Reviews in Control

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“…These residuals are obtained using both physical and analytical redundancy. The details of the solutions can be found in Ozdemir et al [2011].…”

Section: Up-down Counter Solution (Udc)mentioning

confidence: 99%

“…In this paper some of these proposed methods are compared both on test sequences defined in the benchmark, and in addition on a number additional test sets for testing robust of the proposed schemes to operational point of occurrence of the faults. The compared solutions can be seen in Chen et al [2011], Laouti et al [2011], Ozdemir et al [2011], Svard and Nyberg [2011] and Zhang et al [2011]. A number of other solutions have also been applied to this benchmark model, among these are: Ayalew and Pisu [2011], Blesa et al [2011], Dong and Verhaegen [2011], Kiasi et al [2011], Simani et al [2011a], Simani et al [2011b] and Stoican et al [2011].…”

Section: Introductionmentioning

confidence: 99%

Results of a Wind Turbine FDI Competition

Odgaard¹,

Stoustrup

2012

IFAC Proceedings Volumes

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In this paper some newly published methods for fault detection and isolation developed for a wind turbine benchmark model are tested, compared and evaluated. These methods have been presented as a part of an international competition. The tested methods cover different types of fault detection and isolation methods, which include support vector machines, observer based methods, and auto generated methods. All of these methods show interesting potentials for usage in the wind turbine application, but all with different strong and weak sides in relation to the requirements specified in the proposed benchmark model.

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“…The third method relying on Up-Down Counters (UDC) was addressed in [17]. These tools, are commonly used in the aerospace framework, and they provide a different approach to the decision logic usually applied to the control.…”

Section: Performance Verification and Comparisonsmentioning

confidence: 99%

Robust Control Examples Applied to a Wind Turbine Simulated Model

Simani¹,

Castaldi²

2017

Preprint

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Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte-Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

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Wind Turbine Fault Detection Using Counter-Based Residual Thresholding

Cited by 61 publications

References 8 publications

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

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

Results of a Wind Turbine FDI Competition

Robust Control Examples Applied to a Wind Turbine Simulated Model

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