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Sensor Fault Diagnosis in Wind Turbines

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

Abstract

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.

Keywords

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

Nomenclature

ej

jth standard basis vector

f

Additive sensor fault

g

Decision function

is,abc, ir,abc

Stator and rotor three-phase currents

k0

Fault occurrence time

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

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

q

Quantization error

r(i)

ith sample of the residual vector

s(r(i))

Log-likelihood ratio for r(i)

ta

Alarm time instant

us,abc, ur,abc

Stator and rotor three-phase voltages

v,w

Process and measurement noise vectors respectively

x

State vector

y

Output vector

^

Stands for estimate

m

As an upper index indicates measurement

abc

As a lower index indicates three-phase signals

s

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

r

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

*

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

\({\fancyscript{L}}(r(i))\)

Probability law of r(i)

Np

Number of encoder pulses per revolution

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

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

\(Q_{s}^{*}\)

Reactive power reference

Rv, Rw

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

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

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

Tsc

Time window used for speed estimation

Ts

Sampling period

\(T_{g}^{*}\)

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

ω

Frequency

\(\varOmega_{g}\)

Generator speed

\(\eta\)

Portion of the damaged bars of a code wheel

\(\theta_{s}\)

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

θ

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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