fault detection and isolation using kalmanfilter bank for a windturbine generator

7/28/2019 Fault Detection and Isolation using KalmanFilter Bank for a WindTurbine Generator

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Fault Detection and Isolation using

Kalman Filter Bank for a

Wind Turbine Generator


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Objective

To tackle the problem of current sensor Fault Detection and

Isolation (FDI) of a doubly fed induction generator in wind

turbine.

The detection and the isolation of multiple and simultaneous

sensor faults will be treated using a Kalman filter bank

Generalized Observer Scheme

Dedicated Observer Scheme


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DFIG

The doubly feed induction generator (DFIG) is one of the most

used drive In the wind power industry

low cost

simplicity of maintenance

reliability

When a fault occurs, it must be detected as soon as possible,

even where all observed signals remain in their allowable

limits.

The fault must then be located and its cause identified

This aspect becomes more and more investigated because of

the construction of high capacity offshore wind parks.


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The control system operates with the information of the

system provided by sensors, which subjected to faults

Previous Method For Study of Current sensor Fault and

Voltage sensor Fault is Luenberger observers

weve proposed an observer scheme base on Kalman filter to

diagnosticate the current sensor fault of a DFIG because of its

discrete property

which is convenient for the testing on a dSPACE test bench as

well as for a real-time implementation in the future


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Generalised Observer Scheme and


Generalised Observer Scheme Can Isolate Single Sensor Fault

Dedicated Observer Scheme (DOS) Can Isolate a Simultaneous

Fault

Paper Organised Like Operating principle of a wind turbine using doubly feed induction

generator

Modeling of the doubly feed induction generator

The Kalman filter

The Kalman filter bank based on Generalized Observer Scheme and


The results of the FDI for current sensor as well as the real-time

validation are illustrated


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OPERATING PRINCIPLE OF THE WIND TURBINE

USING DFIG


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MODELING of DOUBLY FEED

INDUCTION GENERATOR

In this work, we consider that the DFIG operates at a fixed-

speed

Crotor convertor should be considered as control signals

The stator voltages are the voltages of the grid as known

external inputs.

The model of DFIG was transformed in dq reference frame

The d-axis is chosen to coincide with stator phase r-axis at

t = 0 and

The q-axis leads the d-axis by 90 degree in the direction of

rotation.


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State-Space Representation of the

DFIGx (t) = Ax(t) + Bu(t) + Ds(t)

y(t) = Cx(t) + Ef(t)

x(t) is state vector - [ids iqs idr iqr]T

u(t) is control input, - [Vdr Vqr]T

s(t) is external know input, - [Vds Vqs]T

y(t) is output vector and -- [y1 y2 y3 y4]T

f(t) is fault vector -- [f1 f2 f3 f4]T


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KALMAN FILTER AND DEDICATED OBSERVER

SCHEME

The Kalman filter uses the dynamical model, the know inputs

to that system as well as the measurement (which given by by

sensors) to estimate the state of the system.

xk+1 = Axk+ Buk+ wk

zk= Hxk+ vk

W- Process noise and V- measurement noises

They are supposed to be white noises with normal probability

distributions:

p(w) N(0,Q) Q --process covariance noise

p(v) N(0,R) R-- measurement covariance noise


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The implementation of Kalman filter could be divided in two

steps

Prediction step and Correction step

the diagnostic scheme with Kalman filter is capable to detect

the fault but it is unable to locate the fault.

To resolve this problem, a filter bank will be used in the next.


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Filter bank for the FDI problem

The state observer for fault detection and isolation is a well

known problem

Filter bank used to estimate the dynamical behaviors of the

system in order to detect then to isolate the fault

The first kind of filter bank is Dedicated Observer Scheme(DOS)

The second one, Generalized Observer Scheme (GOS)

Each filter bank is composed by a number of observers, which

are supplied with all of the input and different subsets ofoutput of the system.

A Decision unit diagnosticate whether or not faults are

presented in the sensors and which one is faulty by comparing

the estimated outputs with the measured ones


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Structure Of Generalized Observer

Scheme and Dedicated Observer

Scheme

GOSDOS


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FDI OF THE CURRENT SENSOR FAULTS

For The isolation of the fault the two following fault scenarios

will be used

i) multiple but non simultaneous faults scenario

ii) simultaneous faults scenario.

Fault detection using Kalman filter

Residual rK obtained from the Kalman filter with no sensors

failure. The initial state of the filter was chosen arbitrary.

The sensors faults are detected using the Page-Hinkleys test.

Fault detection and isolation using Generalized ObserverScheme

Fault detection and isolation using Dedicated Observer Scheme

Model in the Loop validation


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Conclusion

In this paper, weve treated the problem of current sensor

Fault Detection and Isolation of a doubly feed induction

generator in wind turbine.

First the use of a Kalman filter to detect sensor fault has been

illustrated. Detection and the isolation of multiple sensor faults was

addressed using the Kalman filter bank in a Generalized

Observer Scheme.

The simultaneous sensor faults was tackled by the DedicatedObserver Scheme.

future work concentrates on the FDI problem for other

sensors of wind turbine (voltage, wind speed for example)

fault detection and isolation using kalmanfilter bank for a windturbine generator

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