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Vector Control of the Brushless Doubly-Fed Machine for Wind Power Generation

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The brushless Doubly-Fed Machine (BDFM) shows commercial benefits in the wind power generation. This paper presents a vector control scheme for the BDFM operating as a variable speed generator (VSG). The proposed vector controller is developed on the power winding stator flux frame, and can be used to control both speed and reactive power. The machine model and the control system are developed in MATLAB. Simulation and experimental results show that the proposed controller can stabilize the BDFM when changes in speed and reactive power are applied.
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ICSET 2008
Vector Control of the Brushless Doubly-Fed
Machine for Wind Power Generation
Shiyi Shao, Ehsan Abdi and Richard McMahon
Electrical Engineering Division, Cambridge University, 9 JJ Thomson Avenue, Cambridge, CB3 0FA, United Kingdom
Phone: +44-01223-748316, Email: ea257@cam.ac.uk
Abstract—The Brushless Doubly-Fed Machine (BDFM) shows
commercial benefits in the wind power generation. This paper
presents a vector control scheme for the BDFM operating as a
variable speed generator (VSG). The proposed vector controller
is developed on the power winding stator flux frame, and can be
used to control both speed and reactive power. The machine
model and the control system are developed in MATLAB.
Simulation and experimental results show that the proposed
controller can stabilize the BDFM when changes in speed and
reactive power are applied.
LIST OF SYMBOLS
v, i, ψ voltage, current and flux
ω1
2the angular frequency of the power
winding and control winding excita-
tion
ωrthe angular velocity of the rotor
θ1
rthe angular position of the power
winding flux frame and the rotor
s1,s
2the slips of both stator windings
p1,p
2the pole pair numbers of power
winding and control winding
Te,T
lthe electrical torque output and the
exerted load torque
Rs1,R
s2,R
rthe resistance of the power winding,
the control winding and the rotor
Ls1,L
s2,L
rself-inductance of stator winding
and rotor [18]
Ls1r,L
s2rcoupling inductance between the sta-
tor winding and the rotor [18]
Im[] imaginary part
complex conjugate
|| magnitude of the vector
subscripts
1,2,r power winding, control winding and
rotor
d, q the direct and quadrant component
on the power winding flux frame
I. INTRODUCTION
The Brushless Doubly-Fed Machine (BDFM) promises sig-
nificant advantages as a variable speed generator [1][2] as
it offers high reliability and low maintenance requirements
by virtue of the absence of brush gear. This is particularly
important as more and more installations are being constructed
offshore and in difficult-to-reach places. In order to progress
the BDFM towards commercial wind power applications, the
machine must be fully controllable so that it can operate at
a specific shaft speed set by wind conditions to gain the
maximum power output.
A number of scalar control algorithms have been developed
for the BDFM, such as open-loop current control [3], closed-
loop frequency control [4] and phase angle control [5][6],
and they were proved to stabilize the BDFM in a wide speed
range. However, vector control (VC) methods, also called field
oriented control (FOC), are known to give better dynamic
performance [7].
A research group at Oregon State University first repre-
sented a vector control system for the BDFM. The controller
is oriented with the rotor flux and aims to control the angle
between the power and control windings’ synchronous frames
[7][8], which increases the complexity and computational
burden. Furthermore, Oregon did not investigate methods of
controlling the reactive power, which is, in fact, important for
power generation applications. Noting that vector controllers
designed for the Doubly-Fed Induction Generator (DFIG)
[9][10] have been widely used in wind generators regulating
both speed and reactive power, Hopfensperger developed a
controller, similar to the one used for the DFIG, for the
Cascade Doubly-Fed Machine (CDFM), whose refrence frame
is aligned with the “side 1 stator flux” [11][12]. The cross
coupling effect was also mentioned. Later, a vector controller
based on the power winding flux was investigated for the
BDFM by Poza [13] with some experimental results presented
on light load. However, the two controllers on the stator
winding flux frame presented in [12][13] were not fully
described. In fact, the development of such control algorithms
is not trivial because of the existence of the cross coupling
compensator.
This paper presents a simplified controller oriented with
the power winding stator flux with a complete mathemati-
cal derivation. Both simulation and experimental results are
provided to demonstrate the performance of the controller.
Experimental tests have been carried out on a 180 frame size
BDFM with a nested-loop rotor. The results show the promise
of using the BDFM for the wind power generation.
II. BDFM OPERATION
The stator of the BDFM is furnished with two separate stator
windings which differ in pole pair numbers to avoid the direct
coupling between the windings. The rotor is specially designed
to couple both of the two stator windings [16]. Generally
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2008 IEEE
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Fig. 1: BDFM operation
speaking, the stator winding 1 is connected to the power
grid directly, and therefore known as the power winding. In
contrast, the stator winding 2, named the control winding, is
supplied with a converter to handle only fractional power. This
configuration, shown in Fig.1, has the advantage of reducing
the rating of the power electronics.
The BDFM can be operated in several modes including the
synchronous (doubly-fed) mode, cascade mode and induction
mode [15]. The synchronous mode is the most desirable mode,
where the shaft speed is independent of the torque exerted on
the machine, and can be expressed as:
ωr=ω1+ω2
p1+p2
(1)
where ω1and ω2are the excitation angular frequencies sup-
plied to the two stator windings.
III. MATHEMATICAL REPRESENTATION OF VECTOR
MODEL
The proposed controller is aligned with the power winding
flux frame. Therefore, ψ1d=|ψ1|and ψ1q=0. The model in
the power winding flux frame is expressed by Equation (2) to
Equation (7) [13][14]:
v1=Rs1i1+1
dt +1ψ1(2)
ψ1=Ls1i1+Ls1rir(3)
v2=Rs2i2+2
dt +j(ω1(p1+p2)ωr)ψ2(4)
ψ2=Ls2i2+Ls2rir(5)
vr=Rrir+r
dt +j(ω1p1ωr)ψr(6)
ψr=Lrir+Ls1ri1+Ls2ri2(7)
and the electric torque is:
Te=3
2p1Im[
ψ1i1]+3
2p2Im[ψ2
i2](8)
Suppose that the BDFM is running in steady state, then the
dynamic model can be transferred to the steady state model,
which is equivalent to the coupled coils model proposed by
Roberts in [18] for a BDFM with a single rotor circuit:
v1=Rs1i1+1Ls1i1+1Ls1rir(9)
s2
s1
v2=s2
s1
Rs2i2+1Ls2i2+1Ls2rir(10)
1
s1
vr=1
s1
Rrir+1Lrir+1Ls1ri1+1Ls2ri2(11)
s1and s2are the slips, which are defined as:
s1
Δ
=ω1p1ωr
ω1
(12)
s2
Δ
=ω2p2ωr
ω2
(13)
IV. CONTROLLER DESIGN
A. Power winding flux estimator
In order to orientate all the quantities in the reference frame,
the angle of the power winding flux, θ1, has to be known.
A common way is using Equation (2) and measuring the
power winding voltage v1while neglecting the power winding
stator resistance Rs1[17]. As a result, the power winding
flux vector lags 90obehind the power winding voltage vector,
and therefore, a subtraction of π/2 from the voltage vector
angle leads to the power winding flux angle θ1, as is shown
in Equation (14). The derivations on the dq frame are given
in Equations (15) and (16).
v1=1ψ1(14)
v1d=ω1ψ1q=0 (15)
v1q=ω1ψ1d=ω1|ψ1|(16)
Since the power winding voltage is connected to the 50 Hz,
240 V grid, the power winding flux has also a constant
magnitude and rotates at fixed 50 Hz and independent to the
machine speed.
B. Control of power winding current
Equation (17) can be obtained by combining Equation (9)
with Equation (11):
v1=(Rs1+s1ω2
1L2
s1r
Rr+js1ω1Lr
+1Ls1)i1+s1ω2
1Ls1rLs2r
Rr+js1ω1Lr
i2
(17)
Spliting Equation (17) into dq components, considering Equa-
tions (15) and (16) and neglecting the power winding stator
resistance Rs1, yields
i2d=Ls1LrL2
s1r
Ls1rLs2r
i1d|ψ1|Lr
Ls1rLs2r
+RrLs1
s1ω1Ls1rLs2r
i1q
(18)
i2q=Ls1LrL2
s1r
Ls1rLs2r
i1q+|ψ1|Rr
s1ω1Ls1rLs2r
RrLs1
s1ω1Ls1rLs2r
i1d
(19)
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The first term of Equation (18),
Ls1LrL2
s1r
Ls1rLs2r
i1d
defines the direct coupling between i2dand i1d, and the
coefficient is constant. The second term,
|ψ1|Lr
Ls1rLs2r
performs as a constant offset. Finally, the third term,
RrLs1
s1ω1Ls1rLs2r
i1q
reflects the cross coupling. Obviously, the only variable is
s1, and therefore this cross coupling term varies with the
shaft speed. The coefficient of the cross coupling term can
be neglected as compared to the direct coupling term if the
operating speed of the rotor is within ±50% of the machine
natural speed.
As a conclusion, i2dis linear with i1dif the effect of the
cross coupling term is neglected. Similar analysis applies to
Equation (19), with a conclusion that i2qis linear with i1q.
C. Control of control winding current
If Equations (4) and (5) are split into dq components, then:
v2d=Rs2i2d+2d
dt (ω1(p1+p2)ωr)ψ2q(20)
v2q=Rs2i2q+2q
dt +(ω1(p1+p2)ωr)ψ2d(21)
ψ2d=Ls2i2d+Ls2rird (22)
ψ2q=Ls2i2q+Ls2rirq (23)
From Equation (3), rotor current ircan be expressed as
ir=ψ1Ls1i1
Ls1r
(24)
Splitting (24) to dq components and substituting Equations
(15) and (16):
ird =ψ1dLs1i1d
Ls1r
=|ψ1|−Ls1i1d
Ls1r
(25)
irq =ψ1qLs1i1q
Ls1r
=Ls1i1q
Ls1r
(26)
Combining with Equations (20), (21), (22), (23), (25) and (26),
and neglecting Rs1, the control winding voltage in the dq
frame can be derived as:
v2d=Rs2i2d+Ls1Ls2LrLs2L2
s1rLs1L2
s2r
Ls1LrL2
s1r
di2d
dt
L2
s1Ls2rRr
s1ω1Ls1r(Ls1LrL2
s1r)
di1q
dt
(ω1(p1+p2)ωr)(Ls2i2qLs2rLs1
Ls1r
i1q)
(27)
v2q=Rs2i2q+Ls1Ls2LrLs2L2
s1rLs1L2
s2r
Ls1LrL2
s1r
di2q
dt
+L2
s1Ls2rRr
s1ω1Ls1r(Ls1LrL2
s1r)
di1d
dt
+(ω1(p1+p2)ωr)(Ls2i2d+Ls2r
|ψ1|−Ls1i1d
Ls1r
)
(28)
Similar analysis as for the control of the power winding
current can be applied to Equation (27). The first term,
Rs2i2d+Ls1Ls2LrLs2L2
s1rLs1L2
s2r
Ls1LrL2
s1r
di2d
dt
shows direct relation between v2dwith i2d. The transfer
function has first order and constant components. The second
term,
L2
s1Ls2rRr
s1ω1Ls1r(Ls1LrL2
s1r)
di1q
dt
is essentially a first order cross coupling, which is also
dependent on the shaft speed. Generally speaking, it can be
neglected compared with the direct coupling term for the same
reason discussed above. The third term,
(ω1(p1+p2)ωr)(Ls2i2qLs2rLs1
Ls1r
i1q)
shows another cross coupling with lower order compared to
both of the first two terms, and therefore can be neglected.
Therefore, if cross coupling is neglected, v2dand i2dhave
a constant and a first order relation. A similar derivation can
be applied to the analysis of Equation (28), concluding that
the v2qand i2qalso have a constant and a first order relation.
D. Control of torque
Considering Equations (8), (15), (16), (25), (26), (27) and
(28), the torque can be expressed as:
Te=3
2(p1+p2)|ψ1|i1q3
2
p2|ψ1|2Rr
s1ω1L2
s1r
3
2
p2RrL2
s1
s1ω1L2
s1r
(i2
1d+i2
1q)
(29)
The first term, 3
2(p1+p2)|ψ1|i1q
shows the fact that the torque of the BDFM is directly related
to i1q. The other term,
3
2
p2|ψ1|2Rr
s1ω1L2
s1r
3
2
p2RrL2
s1
s1ω1L2
s1r
(i2
1d+i2
1q)
shows time-varying relation and can be analyzed like the
control of power winding and control winding current. Con-
sequently, Teis almost linear with i1q.
E. Control of speed
The mechanical differential equation is:
Te=Jr
dt +Tl(30)
where friction force is neglected. Therefore, ωrcan be regu-
lated by controlling Tesince they have a first order relation.
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PI
*
r
ω
+-
r
ω
PI
Var controller
+-
*
1
Q
1
Q
v
2q
v
2d
v
2d
v
2q
r
θ
1
θ
23v
2b
v
2c
PWM
generator PWM
signal
*
2c
v
*
2b
v
*
2a
v
v
2a
Flux
estimator
BDFM
v
1a
v
1b
v
1c
1
θ
Reactive
power
estimator
23
v
1a
v
1b
v
1c
i
1a
i
1b
i
1c
i
1a
i
1b
i
1c
v
1d
v
1q
i
1d
i
1q
Encoder
r
ω
r
θ
Speed controller
Fig. 2: Schematic of the proposed vector system
F. Control of reactive power
The reactive power of the power stator winding is expressed
as [9][10]:
Q1=3
2(v1qi1dv1di1q)(31)
Considering Equations (15) and (16),
Q1=3
2|ψ1|ω1i1d(32)
Therefore, a linear relation exists between Q1and i1d.
G. Proposed vector control scheme
Based on the analysis discussed above, the shaft speed ωr
and reactive power Q1can be controlled by controlling v2q
and v2drespectively:
ωrTei1qi2qv2q
and
Q1i1di2dv2d
Fig.2 shows the speed and reactive power control system
using the proposed control algorithm. The 32 module is
realized by well-known Clark Transformation and Park Trans-
formation [20].
V. E XPERIMENTAL AND SIMULATION RESULTS
A. Experimental rig and simulation setup
An experimental test rig is established in order to validate
the control algorithm, experimental tests have been carried
out on a 180 frame BDFM. Table I gives the physical data
for the machine. A DC machine is mechanically coupled
to the BDFM in order to provide the required torque. An
HBM T30FN torque transducer is used to monitor the torque
data. The speed and position signals are obtained from an
incremental encoder, RP442-z, with the resolution of 2500,
provided by ONO SOKKI. The voltage and the current of each
stator phase are measured by LEM LV 25-p and LEM LTA
100-p respectively. The control system is implemented based
on the xPC Target which receives all the signals mentioned
above. The control loop time delay is 0.2 ms, updating the
three phase voltage waveforms for the control winding. The
outputs of the xPC target are connected to an FPGA-based
PWM generator driving the converter for the BDFM control
winding.
Simulations are also implemented using Simulink, and a
coupled-circuit model for the BDFM [18][19] is developed
to simulate the dynamic performance of the proposed vector
controller. A fixed-step ode5 solver is used, and the step size
is set to 0.4 ms.
B. Experimental and simulation results
In wind power applications, the variable speed generation is
mostly used to enhance the wind turbine efficiency. Therefore,
the control of the rotor speed is critically important in such
applications. Fig.3 illustrates the speed response from 550 rpm
to 720 rpm. Since the DC machine is not equipped with a
suitable controller, in the experiment, the torque varies with
the speed, from 45 Nm to 60 Nm, as shown in Fig.3b. The
rise time of the speed, shown in Fig.3a, is less than 5 s. The
reactive power is set to be constant 1 kVAR. As is shown in
Fig.3c, there is an overshoot of about 2 kVAR in the reactive
power, but it quickly settles down to the reference value. The
simulation adopts the same setup applied in the experiment,
and produces similar results as from Fig.3d to Fig.3f.
The proposed vector controller also has the ability to reg-
ulate the reactive power of the BDFM power winding, which
is paramountly attractive in wind power generation. Fig.4a
shows the experimental results of reactive power changing
from 2 kVAR to 0.5 kVAR and then back to 2 kVAR, while
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0 5 10 15 20
500
550
600
650
700
750
Time (s)
Speed (rpm)
Ref Speed
Exp Speed
(a) Experimental speed
0 5 10 15 20
30
40
50
60
70
Time (s)
Torque (Nm)
(b) Experimental torque
0 5 10 15 20
1000
500
0
500
1000
1500
2000
2500
Time (s)
Reactive Power (VAR)
(c) Experimental reactive power
0 5 10 15 20 25
500
550
600
650
700
750
Time (s)
Speed (rpm)
Ref Speed
Sim Speed
(d) Simulated speed
0 5 10 15 20 25
30
40
50
60
70
Time (s)
Torque (Nm)
(e) Simulated torque
0 5 10 15 20 25
500
0
500
1000
1500
2000
2500
3000
Time (s)
Reactive Power (VAR)
(f) Simulated power
Fig. 3: Simulation and experimental results of speed change response, 40 to 60 Nm, 1 kVAR
010 20 30 40
500
0
500
1000
1500
2000
2500
3000
Time (s)
Reactive Power (VAR)
Ref Reactive Power
Exp Reactive Power
(a) Experimental reactive power
010 20 30 40
500
520
540
560
580
600
Time (s)
Speed (rpm)
(b) Experimental speed
0 5 10 15 20 25
500
0
500
1000
1500
2000
2500
3000
Time (s)
Reactive power (VAR)
Ref Reactive Power
Sim Reactive Power
(c) Simulated reactive power
0 5 10 15 20 25
500
520
540
560
580
600
Time (s)
Speed (rpm)
(d) Simulated speed
Fig. 4: Simulation and experimental results of reactive power change, 550 rpm, no load
0 5 10 15 20 25 30
20
0
20
40
60
80
Time (s)
Torque (Nm)
(a) Experimental torque
0 5 10 15 20 25 30
400
450
500
550
600
650
700
Time (s)
Speed (rpm)
(b) Experimental speed
010 20 30
1000
0
1000
2000
3000
Time (s)
Reactive Power (VAR)
(c) Experimental reactive power
0 5 10 15 20 25 30
20
0
20
40
60
80
Time (s)
Torque (Nm)
(d) Simulated torque
0 5 10 15 20 25 30
400
450
500
550
600
650
700
Time (s)
Speed (rpm)
(e) Simulated speed
010 20 30
500
0
500
1000
1500
2000
2500
3000
Time (s)
Reactive Power (VAR)
(f) Simulated reactive power
Fig. 5: Simulation and experimental results of torque change, 550 rpm, 1 kVAR
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TABLE I: Prototype Machine Specifications
Parameter Value
Frame size D180
Stator 1 pole-pairs 2
Stator 2 pole-pairs 4
Stator slots 48
Rotor slots 36
Rotor design Nested-loop design [18]
the speed keeps constant at 550 rpm as shown in Fig.4b. The
experiment is done at no load. The simulation results in Fig.4c
and Fig.4d validate the experimental results.
Finally, the torque change from 0 to 60 Nm is supplied to
the BDFM to test the stability of the controller in Fig.5a. Both
speed and reactive power come back to the reference values
with an overshoot shown in Fig.5b and Fig.5c respectively,
which displays the stability under the applied vector controller.
Again, similar simulation results have been obtained from
Fig.5d to Fig.5f.
VI. CONCLUSIONS
This paper proposes a simplified power winding flux ori-
ented vector control scheme without the cross coupling com-
pensator. Detailed theoretical analysis is done to represent
the decoupled control structure of the speed and reactive
power control. The controller is able to regulate both the
rotor speed and reactive power. The machine model and the
controller have been implemented in MATLAB and verified
experimentally. The experimental results have shown stable
operation of the machine under various controlling commands.
The performance obtained further reinforces the suitability of
the BDFM for wind power generation.
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... Recent research has illustrated the advantages of the brushless doubly-fed machine (BDFM) in motor drive and generator system applications promise significant advantages for wind power generation, as they offer high reliability and low maintenance requirements by virtue of absence of a brush gear [6]. ...
... The different control strategies that have been used until now in the BDFM are the scalar current control [8], the direct torque control [20], L2 robust control method [18], H∞ control [19], fuzzy power control [9,18], sliding mode power control [19], the rotor flux oriented controlled [3], a new vector controller using a dynamic model with a unified reference frame based on the power winding flux was investigated for the BDFM by Poza [13], and a simplified controller oriented with the power winding stator flux with a complete mathematical derivation frame presented by Shiyi [6,7] with some experimental results presented on both speed and reactive power regulating. ...
Article
Full-text available
This paper presents the applications of the sliding mode control to a brushless doubly fed induction generator (BDFG) used in wind-energy conversion systems. The controller is designed based on the sliding mode control combined with a the stator power winding flux oriented vector principle, the independent control of active and reactive powers has been developed and the performance of proposed the block diagram of the variable speed constant-frequency (VSCF) wind energy generation system is validated in the Matlab/Simulink environment and the computer simulation results obtained confirm the effectiveness of this control strategy.
... Typically the two stator supplies are of different frequencies, one a fixed frequency supply connected to the grid, and the other a variable frequency supply derived from a power electronic frequency converter (inverter), as illustrated in figure (1), the natural synchronous speed of the machine. BDFM in steady-state operation shows a synchronous speed equal to: Recent research has illustrated the advantages of the brushless doubly-fed machine (BDFM) in motor drive and generator system applications promise significant advantages for wind power generation, as they offer high reliability and low-maintenance requirements by virtue of absence of a brush gear [6]. ...
... Suppose that the BDFM is running in steady state, then the dynamic model can be transferred to the state model [6], [16]. ...
Conference Paper
Full-text available
In this paper, the Mathematics model of brushless doubly –fed induction generator (BDFG) using the power winding flux reference model is presented the control strategy for flexible power flow control is developed by applying power winding flux oriented vector control (technique) ,the simulation results verified the control algorithm and the active and reactive power independently and stably. Keywords – Brushless doubly-fed machine, flux oriented vector control, active and reactive power.
... The traditional control schemes used for the BDFIM include vector control [5,6] and direct torque control (DTC) [7][8][9][10]. The vector conversion needed in the implementation of vector control may be complex and timeconsuming. ...
Conference Paper
Full-text available
This paper proposes a modulated model predictive control (MMPC) algorithm for a brushless double-fed induction machine. The Brushless Doubly-Fed Induction Machine has some important advantages over alternative solutions for brushless machine applications. The proposed modulation technique achieves a fixed switching frequency, which gives good system performance. The paper examines the design and implementation of the modulation technique and simulation results verify the operation of the proposed modulation technique.
... Many control strategies have been proposed for the variable speed drive applications of BDFMs, such as phase-angle control [7], indirect stator-quantities control [8], and direct torque control [9]. Some control strategies for the BDFMs used as grid-connected wind generators have also been studied, such as the stator-flux-oriented vector control proposed by S. Shao et al. [10]- [12]. ...
Article
This paper presents a stand-alone variable speed constant frequency (VSCF) ship shaft generator system based on a brushless doubly-fed machine (BDFM). In this system, the output voltage amplitude and frequency of the BDFM are kept constant under a variable rotor speed and load by utilizing a well-designed current vector controller to regulate the control winding (CW) current. The control scheme is proposed, and the hardware design for the control system is developed. The proposed generator system is tested on a 325 TEU container vessel, and the test results show the good dynamic performance of the CW current vector controller and the whole control system. A harmonic analysis of the output voltage and a fuel consumption analysis of the generator system are also implemented. Finally, the total efficiency of the generator system is presented under different rotor speeds and load conditions.
Article
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When the dynamic mathematical model of asynchronous motor is established, it usually ignores the motor iron loss. Actually, the motor iron loss exists and it has a great influence on the performance of the motor, especially on the efficiency. A motor model considering iron loss is built in this paper and compared with the motor model ignoring iron loss by Simulink. The paper provides a practical model that has high-performance control and it is closer to the actual physical behaviour of asynchronous motors. The simulation results verify the correctness, validity and accuracy of the model and the conclusions provide a theoretical reference for the research of asynchronous motor and the efficiency optimal design of its control system.
Conference Paper
This paper discusses a problem of parameters variation in the control strategy of an instantaneous power with oriented flux applied to the brushless doubly fed induction generators (BDFIG) for power generation. The aim of BDFIG control is to achieve dynamic performances similar to the doubly fed induction generator (DFIG). The objective is to apply a robust control to independently control active and reactive power generated by the BDFIG decoupled by the orientation of flux without parameters variation impact. For this, we use an online estimation with a new extended KALMAN filter only for the preponderant parameters. Simulation results confirm the feasibility and superb performance of the proposed strategy.
Article
A direct torque control(DTC) strategy in static reference frame for brushless doubly-fed induction machine is presented in this paper. The estimation method for electromagnetic torque is deduced, that is, to observe fluxes and currents of power winding(PW) and control winding(CW) in the local reference frame respectively, and then estimating the torque. Thus rotation coordinate transformation is prevented and parameters used in system are reduced. In addition, the switch table applied to both sub-synchronous and super-synchronous is constructed. The system's structure is as simple as that for the DTC of induction machine, except that voltage and current of one more stator winding are required. The experimental results on a prototype show that the estimation of torque is stable, the speed for BDFIM is stably controlled from super-synchronous to sub-synchronous, and the controller can stabilize the system and maintain the desired speed when sudden torque changes are applied.
Article
One of the key factors influencing the applications of brushless doubly-fed machine (BDFM) is its operating stability. The stability of the BDFM with cage rotor was studied by building up its small signal model and using the Lyapunov criterion. The open-loop control strategy with constant ratio of control winding voltage to frequency was analyzed and given. And then, an improved closed-loop control strategy of control winding current with the cross-compensatory quantity was proposed. Furthermore, the influences of different factors on the stability of the BDFM with cage rotor were discussed, such as the control strategy, the speed, the load torque, the moment of inertia and the ratio of control winding voltage to frequency. The analysis results of the small signal model were verified by the Matlab simulation. The results show that the proposed method of stability analysis and the simulation model are correct, and the improved closed-loop control strategy of control winding current is superior to the open-loop control strategy with the constant ratio of control winding voltage to frequency.
Article
To further study the steady-state characteristics of brushless doubly-fed machine (BDFM) with squirrel-rotor, the basic equations are given according to the coupling circuits of BDFM. And then, the mathematical models of BDFM current, active power, reactive power, electromagnetic torque and power factor are analyzed and derived respectively. Furthermore, the characteristics of torque-angle, torque-frequency, power-angle, reactive power, power factor and V-shaped curve of BDFM are obtained respectively by MATLAB simulation of a prototype. The energy conversion of BDFM is analyzed by the proposed mathematical models at the same time. The results show that all the steady-state characteristics of BDFM can be expressed as the functions of the control windings voltage, frequency and the power angle. The proposed model simplifies the analysis of the steady-state characteristics of BDFM and provides a theoretical foundation for the further study on operating stability and control strategy of BDFM.
Article
By deducing the mathematical model of cascade brushless doubly-fed machine (CBDFM) in rotor coordinate, the model in control machine synchronous coordinate was derived. Based on analysis to this model, it could be concluded that the machine's torque and control machine's flux could be controlled effectively by the cross and direct axis component of the control machine's stator current. According to this conclusion, the CBDFM control method was then designed. With outer-loop as flux and torque loop, inner-loop as current loop, the control of total torque and control machine's flux amplitude was then realized. In the meantime, this control algorithm was simple and avoided solving complex nonlinear equation. At last, simulation results proved the effect of this method.
Thesis
Full-text available
The Brushless Doubly-Fed Machine (BDFM) shows promise as a variable speed drive and generator. The BDFM is particularly attractive for use as a generator in wind turbines as the machine's brushless operation reduces maintenance requirements. However, a deeper understanding of the machine is needed before full size generators can be designed. This dissertation contributes towards this goal through machine analysis, modelling and instrumentation. A system of measuring rotor bar currents in real-time is developed using a Rogowski probe to transduce the signal and Bluetooth wireless technology to transmit data from a moving rotor back to a computer for logging and analysis. The design of the rotor is critical to good performance and direct measurements of rotor currents would help to build confidence in rotor performance as machine sizes increase. As well as verifying theoretical predictions, measurements of rotor currents are employed to acquire parameter values for machine models. A coupled-circuit model is developed for a general class of BDFMs. A simple analytical method to calculate the parameter values is presented. An equivalent circuit model is derived from the coupled-circuit model by performing suitable transformations. The order of the rotor states is reduced to allow parameter values to be computed for a simple equivalent circuit representation of the machine. Both coupled-circuit and equivalent circuit models are verified by experimental tests on a prototype BDFM. An experimental method of parameter estimation is developed for the equivalent circuit model, based on the curve-fitting approach. Three widely adopted optimisation algorithms are implemented as the solution methods to the nonlinear problem. The proposed algorithms are compared with respect to their performance, computational cost and simplicity. Rotor current measurements are employed to estimate the parameter values for the full equivalent circuit. A method of obtaining the rotor current in the equivalent circuit from the measured bar currents is presented. The effects of iron saturation in the BDFM modelling are investigated. A method of calculating the parameter values for the coupled-circuit model, taking tooth saturation into account, is presented. The model is able to calculate the flux density in the machine air gap and stator and rotor teeth. These flux densities are also measured using the flux search coils. The issue of the specific magnetic loading for the BDFM is discussed and its calculation from the fundamental components of the air gap flux density is presented. The equivalent circuit parameter values are derived from the coupled-circuit model and from experimental tests under saturation. It is shown that the predictions of the equivalent circuit model are within acceptable accuracy if its parameter values are obtained at the same operating specific magnetic loading.
Article
Full-text available
The paper presents experimental results to assess the performance of a variety of rotors used in a Brushless Doubly Fed Machine (BDFM). In the experiments the torque-speed characteristics were measured on a BDFM fitted with four rotors with five different windings. The measurements were made of the machine excited with just one stator supply with the second stator supply first open circuit, and then short- circuited. The results give valuable insight into how different rotors, including a novel design of BDFM rotor, will perform in a BDFM configured as a variable speed generator. The results highlight important differences between the rotors related to their winding construction.
Conference Paper
Full-text available
The Brushless Doubly-Fed Machine (BDFM) is attractive for use in wind turbines, especially offshore, as it offers high reliability by virtue of the absence of brushgear. Critical issues in the use of the BDFM in this role at a system level include the appropriate mode of operation, the sizing of associated converter and the control of the machine. At a machine level, the design of the machine and the determination of its ratings are important. Both system and machine issues are reviewed in the light of recent advances in the study of the BDFM, and preliminary comparisons are made with the well-established doubly fed wound rotor induction generator.
Conference Paper
Full-text available
A simple method of controlling the Brushless Doubly-Fed Machine (BDFM) is presented. The controller comprises two Proportional-Integral (PI) modules and requires only the rotor speed feedback. The machine model and the control system are developed in MATLAB. Both simulation and experimental results are presented. The performance of the system is presented in the motoring and generating operations. The experimental tests included in this paper were carried out on a 180 frame size BDFM with a nested-loop rotor.
Conference Paper
Full-text available
This paper presents dynamic and steady-state performance of the Brushless Doubly-Fed Machine (BDFM) operating as a variable speed drive. A simple closed-loop control system is used which only requires a speed feedback. The controller is capable of stabilising the machine when changes in speed and torque are applied. The machine starts in cascade mode and then makes a transition to the synchronous mode to reach the desired speed. This will allow a uni-directional converter to be used. The experiments included in this paper were carried out on a 180 frame size BDFM.
Conference Paper
The brushless Doubly-Fed Induction Machine (DFIM) has many advantages over the conventional DFIG commonly applied in wind turbines. However, due to the complex motion of the magnetic field in this machine type, the inclusion of nonlinear iron saturation in brushless DFIM models has been proven to be challenging. This paper combines a brushless DFIM Electric Equivalent Circuit (EEC) model with an analytical derived magnetic field model. The saturated magnetic field is iteratively obtained using the secant method. Saturation is included in the EEC model by introducing complex saturation factors derived from the magnetic field. This results in an EEC model that is able to accurately determine brushless DFIM operating characteristics. The model is validated by application to a case study machine and comparing the results with those derived from a finite element model.
Article
The self-cascaded machine is a single-unit version of two separate induction machines connected in cascade. In construction, however, it closely resembles a conventional induction motor. It may be run asynchronously, with resistance control if required, or synchronously without any external connections to the rotor winding. As a motor it is particularly suited to low-speed duties. With its simple and robust form of rotor construction, and the absence of slip rings or rotating diodes, it is also ideally suited for operation at high speeds as a high-frequency brushless alternator. A general description of the machine, and the underlying principles involved, are presented. A theoretical treatment is included based on doubly stator-fed operation of the machine. General performance equations are derived, and equivalent static networks are obtained for steady-state operating conditions.
Book
This is the first comprehensive book on sensor less high performance a.c. drives. It is essential reading for anyone interested in acquiring a solid background on sensor less torque-controlled drives. It presents a detailed and unified treatment of sensor less vector-controlled and direct-torque controlled drive systems. It also discusses the applications of artificial intelligence to drives. Where possible, space vector theory is used and emphasis is laid on detailed mathematical and physical analysis. Sensorless drive schemes for different types of permanent magnet synchronous motors, synchronous reluctance motors, and induction motors are also presented. These include more than twenty vector drives e.g. five types of MRAS-based vector drives, and eleven types of direct-torque-controlled (DTC) drives, e.g. the ABB DTC drive. However, torque-controlled switched reluctance motor drives are also discussed due to their emerging importance. The book also covers various drive applications using artificial intelligence (fuzzy logic, neural networks, fuzzy-neural networks) and AI-based modelling of electrical machines. Finally, self-commissioning techniques are also discussed. This is a comprehensive thoroughly up-to-date, and self-contained book suitable for students at various levels, teachers, and industrial readership. Peter Vas is a Professor at the Department of Engineering at the University of Aberdeen, UK, where he is also the Head of the Intelligent Motion Control Group. His previous books published by Oxford University Press are extensively used worldwide.
Article
An abstract is not available.
Conference Paper
Brushless doubly-fed machines attempt to achieve the benefits of adjustable speed drives while minimizing power converter rating and cost. However, since only one set of stator windings is controllable, control of the resulting motor drive is potentially more complex than for conventional singly-fed systems. While the doubly-fed machine system is open-loop stable, dynamic response and steady state performance can be far from optimum in the open-loop mode of operation. This has prompted the development of closed-loop control algorithms ranging from simple stabilizing speed feedback and power factor adjustment to fast, predictive torque control based on instantaneous error. The present paper discusses the laboratory implementation of proposed control schemes for brushless doubly-fed drives