Fault detection of a benchmark wind turbine using interval analysis

Seyed Mojtaba Tabatabaeipour, Peter Fogh Odgaard, Thomas Bak

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

10 Citations (Scopus)
743 Downloads (Pure)

Abstract

This paper investigates a state estimation set- membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine.
Original languageEnglish
Title of host publication2012 American Control Conference (ACC)
Number of pages6
Publication date27 Jun 2012
Pages4387-4392
ISBN (Print)978-1-4577-1095-7
ISBN (Electronic)978-1-4673-2102-0
Publication statusPublished - 27 Jun 2012
Event2012 American Control Conference - Montreal, Canada
Duration: 27 Jun 201229 Jun 2012

Conference

Conference2012 American Control Conference
CountryCanada
CityMontreal
Period27/06/201229/06/2012
SeriesAmerican Control Conference
ISSN0743-1619

Fingerprint

Fault detection
Wind turbines
Aerodynamics
Torque
State estimation
Nonlinear systems
Turbines

Keywords

  • Fault Detection
  • wind turbine

Cite this

Tabatabaeipour, S. M., Odgaard, P. F., & Bak, T. (2012). Fault detection of a benchmark wind turbine using interval analysis. In 2012 American Control Conference (ACC) (pp. 4387-4392). American Control Conference
Tabatabaeipour, Seyed Mojtaba ; Odgaard, Peter Fogh ; Bak, Thomas. / Fault detection of a benchmark wind turbine using interval analysis. 2012 American Control Conference (ACC). 2012. pp. 4387-4392 (American Control Conference).
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Tabatabaeipour, SM, Odgaard, PF & Bak, T 2012, Fault detection of a benchmark wind turbine using interval analysis. in 2012 American Control Conference (ACC). American Control Conference, pp. 4387-4392, 2012 American Control Conference, Montreal, Canada, 27/06/2012.

Fault detection of a benchmark wind turbine using interval analysis. / Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas.

2012 American Control Conference (ACC). 2012. p. 4387-4392 (American Control Conference).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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N2 - This paper investigates a state estimation set- membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine.

AB - This paper investigates a state estimation set- membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine.

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Tabatabaeipour SM, Odgaard PF, Bak T. Fault detection of a benchmark wind turbine using interval analysis. In 2012 American Control Conference (ACC). 2012. p. 4387-4392. (American Control Conference).