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Robust model-based fault diagnosis of mechanical drive train in V47/660 kW wind turbine

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Abstract

In this study, a robust fault diagnosis scheme for V47/660 kW wind turbine is proposed. A comprehensive mathematical model for mechanical drive train and gearbox dynamic of V47/660 kW wind turbine operating in Manjil wind farm, Gilan province, Iran, is developed based on which the model-based Fault Detection and Isolation (FDI) scheme is designed. The proposed FDI scheme detects various critical and common sensor faults, actuator faults and components faults. A mixed Unknown Input-Proportional Integral Observer (UI-PIO) method and the parameter estimation method are used for fault detection and isolation. The robustness of the residuals to disturbances is also addressed. Simulation results using experimental data are presented to demonstrate the effectiveness of the proposed fault diagnosis system.

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Abbreviations

A :

Tooth surface

C :

Damping

\(C_{p}\) :

Power coefficient

d(t):

Unknown input

\(D_{n}\) :

Viscous damping for nth contacting tooth pair

\(d_{s}\) :

Measurement noise

\(E_{d}\) :

Unknown input distribution matrix

\(E_{s}\) :

Sensor noise distribution matrix

\(e_{y}\) :

Output estimation error

\({F_{a}}\) :

Actuator fault distribution matrix

\({F_{t}}\) :

Thrust force that wind apply to the rotor disk

h :

Film thickness

J :

Inertia

K :

Stiffness

\(L_{1}\) :

Stator inductance

\(L_{2}\) :

Rotor inductance

\(L_{m}\) :

Mutual inductance

m :

Equivalent mass

\(m_{g}\) :

Module of gear

n :

Gearbox ratio

P :

Wind Power

p :

Number of pole pairs

\(P_{r}\) :

Mechanical energy of rotor

\(p_{nom}\) :

Nominal value of parameters

\(\hat{{p}}_k \) :

Estimated value of parameters

R :

Rotor radius

\(R_{1}\) :

Stator resistance

\(R_{2}\) :

Rotor resistance

\(T_{g}\) :

Generator torque

\(T_{g,r}\) :

Reference torque

\(T_{hs}\) :

High speed shaft torque

\(T_{ls}\) :

Low speed shaft torque

\(T_{rd}\) :

Stiffness cycle time

\(T_{rot}\) :

Rotor torque

\(V_{e}\) :

Root mean square value of supply voltage

v :

Wind speed

\(v_{m}\) :

Constant part of wind speed

\(v_{t}\) :

Turbulent part of wind speed

\(w_{a}\) :

Supply frequency

\(w_{g}\) :

Generator speed

\(w_{i}\) :

Rotor frequency of generator

\(w_{rot}\) :

Rotor speed

\(\alpha \) :

Pressure angle

\(\beta \) :

Pitch angle

\(\beta ^{\prime }\) :

Probability of increase in residual from random value

\(\delta \) :

Shaft’s twist

\(\varepsilon \) :

Contact ratio

\(\eta \) :

Lubricant viscosity

\(\theta \) :

Torsional displacement

\(\lambda \) :

Tip speed ratio

\(\rho \) :

Air density

\(\psi \) :

Tooth bending deflection

\(\varsigma \) :

Momentum parameter

\(\tau _{T}\) :

Generator time constant

\(\phi \) :

Regressor vector

\(\omega \) :

Angular velocity

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Authors and Affiliations

  1. Department of Electrical Engineering, Shahid Beheshti University, P.O. Box: 16765-1719, Tehran, Iran

    Shadi Asgari & Alireza Yazdizadeh

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  1. Shadi Asgari
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  2. Alireza Yazdizadeh
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Correspondence to Shadi Asgari.

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Asgari, S., Yazdizadeh, A. Robust model-based fault diagnosis of mechanical drive train in V47/660 kW wind turbine. Energy Syst 9, 921–952 (2018). https://doi.org/10.1007/s12667-017-0231-2

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