ECN: FTC

ECN

Fault-Tolerant Control

Fault-Tolerant Control (FTC) is one of the four major control components of the integrated wind turbine control concept SucCon, which is being developed in the SenterNovem research project number EOS LT02013 “Sustainable Control: A new approach to operate wind turbines”. The goal is to avoid unnecessary standstill as a result of component degradation. 

To achieve fault-tolerance, an FTC system consisting of the following components has been developed:

  • Residual generator: an FTC subsystem that uses input/output measurements from the wind turbine and generates a residual signal in such a way, that its mean value is affected by failures, but not affected by the wind turbulence. Different methods for constructing such residuals have been investigated, including Kalman filter and Hinf/H_ optimal observer.
  • Decision system: processes the residual signal to detect changes in its mean value, indicating failures. To this end, two methods are investigated, cumulative sum method (CUSUM) and generalized likelihood ratio test (GLRT).
  • Reconfiguration: here it is decided whether the controller can continue operation using healthy sensor/actuator instrumentation, or should be safely switched off.

In order to continue operation with no performance degradation, some kind of redundancy is required. For instance, the individual pitch controller (IPC) requires measurements of the out-of-plane blade root bending moments. When strain gauges are used, it is reasonable to implement double redundancy (i.e. two sensors per blade) due to the relatively high failure rate of these devices.  Such double redundancy would not only make it possible to isolate a faulty sensor, but allows keeping the IPC controller operational until reparation can take place. 

Publications

Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques
Xiukun Wei, Michel Verhaegen and Tim van Engelen,
to appear in International Journal of Robust and Nonlinear Control, 2009.

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