A Fault Tolerant Control Approach to Sustainable Offshore Wind Turbines

  • Montadher Sami ShakerEmail author
  • Ron J. Patton
Part of the Advances in Industrial Control book series (AIC)


The main challenges for the deployment of wind turbine systems are to maximise the amount of good quality electrical power extracted from wind energy. This must be ensured over a significantly wide range of weather conditions simultaneously with minimising both manufacturing and maintenance costs. In consequence to this, the fault tolerant control (FTC) and fault detection and diagnosis (FDD) research have witnessed a steady increase in interest in this application area as an approach to maintain system sustainability with more focus on offshore wind turbines (OWTs) projects. This chapter focuses on investigations of different aspects of operation and control of wind turbine systems and the proposal of a new FTC approach to sustainable OWTs. A typical non-linear state space model of a wind turbine system is described and a Takagi-Sugeno (T-S) fuzzy model of this system is also presented. A new approach to active sensor fault tolerant tracking control (FTTC) for OWT described via T-S multiple models. The FTTC strategy is designed in such way that aims to maintaining nominal wind turbine controller without change in both fault and fault-free cases. This is achieved by inserting T-S proportional state estimators augmented with multiple-integral feedback (PMI) fault estimators to be capable to estimate different generator and rotor speed sensors fault for compensation purposes. The material in this chapter is presented using a non-linear benchmark system model of a wind turbine offered within a competition led by the companies Mathworks and KK-Electronic.


Wind turbine control Active fault tolerant control Fault estimation T-S fuzzy systems Tracking control 


\(P_{\text{cap}} , P_{\text{wind}}\)

Aerodynamic, wind power


Air density


Rotor radius

\(C_{p} , C_{q}\)

Power, torque coefficients

\(\beta , \beta_{r}\)

Actual, reference blade pitch angle

\(\lambda , \lambda_{\text{opt}}\)

Actual, optimal tip-speed-ratio

\(v, v_{ \hbox{min} } , v_{ \hbox{max} }\)

Point, minimum, and maximum wind speed

\(\omega_{r} , \omega_{ \hbox{min} } , \omega_{ \hbox{max} } , \omega_{{r{\text{opt}}}}\)

Actual, minimum, maximum, and optimal rotor speed


Aerodynamic torque

\(T_{g} , T_{gr} , T_{gm} ,\)

Actual, reference, measured generator torque

\(J_{r} , J_{g}\)

Rotor, generator inertia

\(B_{r} , B_{g}\)

Rotor, generator external damping


Generator speed


Gearbox ratio

\(K_{{{\text{d}}t}} ,B_{{{\text{d}}t}}\)

Torsion stiffness, damping coefficients

\(\theta_{\Delta }\)

Torsion angle


Damping factor


Natural frequency


Generator time constant

\(C_{{p{ \hbox{max} }}}\)

Upper bound of power coefficient

\({\mathcal{A}}_{\text{wind}} ,\text{ }\,{\mathcal{A}}, \,{\mathcal{A}}_{2}\)

Upstream, disc, downstream, areas

\(P^{ + } ,\, P^{ - }\)

Pressure before, after actuator disc


Thrust exerted on the actuator disc


Axial interference factor


Blade, turbulence times


Length of the disturbed wind


Number of blades


Sensor fault signal

\(e_{t} , \,e_{x} ,\, e_{v}\)

Tracking, state estimation, wind measurement errors

\(K\left( p \right), \,L_{a} \left( p \right)\)

Controller, observer gains

\(P_{1} ,\,P_{2} ,\,\gamma , \,\mu , \,X_{1}\)

LMI variables

\(A\left( p \right),\,B,\, E\left( p \right), \,C, \,D_{f}\)

System matrices

\(\bar{A}\left( p \right),\, \bar{B}, \,\bar{E}\left( p \right), \,R, \bar{C},\, \bar{D}_{f}\)

System matrices augmented with tracking error integral

\(A_{a} \left( p \right), \,B_{a} , \,E_{a} \left( p \right), \,R_{a} , \,G, \,C_{a}\)

Observer augmented matrices


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of TechnologyBaghdadIraq
  2. 2.School of EngineeringUniversity of HullHullUK

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