fault detection and isolation using kalmanfilter bank for a windturbine generator
TRANSCRIPT
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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
Dedicated Observer Scheme
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
Dedicated Observer Scheme
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)