Sensor Fault Diagnosis in Wind Turbines

  • Manuel Gálvez-CarrilloEmail author
  • Laurent Rakoto
  • Michel Kinnaert
Part of the Advances in Industrial Control book series (AIC)


This chapter addresses the early detection and isolation of sensor faults in a systematic and unified way and illustrates the approach on wind turbine simulation data. Three problems are successively considered: individual signal monitoring, fault detection and isolation (FDI) in redundant sensors, and FDI based on analytical redundancy. In all three cases, a specific approach to generate fault indicators, also called residuals, is presented and combined with an online statistical change detection/isolation algorithm. The considered case studies consist of wind turbine generator speed monitoring, as well as FDI in the stator current and voltages of a wind-driven doubly fed induction generator. For the latter problem, the fact that the three-phase signals are balanced allows one to determine a simple signal model from which a multiobserver scheme is designed for residual generation.


Sensor fault detection and isolation Statistical change detection/isolation algorithm Multiobserver scheme Three-phase signals Doubly fed induction generator 



jth standard basis vector


Additive sensor fault


Decision function

is,abc, ir,abc

Stator and rotor three-phase currents


Fault occurrence time

\(p_{\theta } (r(i))\)

Probability density function of r(i) that depends on the vector parameter θ


Quantization error


ith sample of the residual vector


Log-likelihood ratio for r(i)


Alarm time instant

us,abc, ur,abc

Stator and rotor three-phase voltages


Process and measurement noise vectors respectively


State vector


Output vector


Stands for estimate


As an upper index indicates measurement


As a lower index indicates three-phase signals


As a lower index indicates a stator signal (voltage or current)


As a lower index indicates a rotor signal (voltage or current)


As an upper index indicates a reference signal within a closed loop


Probability law of r(i)


Number of encoder pulses per revolution

\({\fancyscript{N}}(\mu_{0} ,\varSigma )\)

Normal distribution with mean \(\mu_{0}\) and variance \(\varSigma\)


Reactive power reference

Rv, Rw

Variance of \({\mathbf{v}}\) and \({\mathbf{w}}\) respectively


Set of k independent residual samples

\({\fancyscript{S}}({\fancyscript{R}}_{1}^{\fancyscript{k}} )\)

Log-likelihood ratio for the data set \({\fancyscript{R}}_{1}^{k}\), assuming a change in the mean at time k 0


Time window used for speed estimation


Sampling period


Generator torque reference

\({\fancyscript{U}}\left( {a,b} \right)\)

Probability law of a uniform statistical distribution in the interval [a,b]

\(\varLambda_{{k_{0} }} ({\fancyscript{R}}_{1}^{k} )\)

Likelihood ratio for the data set \({\fancyscript{R}}_{1}^{k}\), assuming a change in the mean at time k 0




Generator speed


Portion of the damaged bars of a code wheel


Angle that fulfills \(\omega_{s} = \frac{{{\text{d}}\theta_{s} }}{{{\text{d}}t}}\)


Vector that parameterizes a probability density function


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Manuel Gálvez-Carrillo
    • 1
    Email author
  • Laurent Rakoto
    • 2
  • Michel Kinnaert
    • 2
  1. 1.ELIA System Operator S.A.BrusselsBelgium
  2. 2.Université Libre de Bruxelles (ULB)BrusselsBelgium

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