A review on position speed sensorless control for permanent magnet synchronous machine based wind energy conversion systems

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 1, NO. 4, DECEMBER 2013

203

A Review on Position/Speed Sensorless Control for
Permanent-Magnet Synchronous Machine-Based
Wind Energy Conversion Systems
Yue Zhao, Student Member, IEEE, Chun Wei, Student Member, IEEE, Zhe Zhang, Student Member, IEEE,
and Wei Qiao, Senior Member, IEEE

Abstract— Owing to the advantages of higher efficiency,
greater reliability, and better grid compatibility, the directdrive permanent-magnet synchronous generator (PMSG)-based
variable-speed wind energy conversion systems (WECSs) have
drawn the highest attention from both academia and industry in
the last few years. Applying mechanical position/speed sensorless
control to direct-drive PMSG-based WECSs will further reduce
the cost and complexity, while enhancing the reliability and
robustness of the WECSs. This paper reviews the state-of-theart and highly applicable mechanical position/speed sensorless
control schemes for PMSG-based variable-speed WECSs. These
include wind speed sensorless control schemes, generator rotor
position and speed sensorless vector control schemes, and direct
torque and direct power control schemes for a variety of directdrive PMSG-based WECSs.
Index Terms— Direct drive, permanent-magnet synchronous
generator (PMSG), sensorless control, variable speed, wind
energy conversion system (WECS).

I. I NTRODUCTION

T

HE total installed capacity of wind power is growing

tremendously in the global market. According to a report
of the world wind energy association [1], the worldwide
wind power installation has reached 254 GW by the end of
June 2012. Among various wind energy conversion system
(WECS) configurations, the doubly-fed induction generator
(DFIG)-based variable-speed WECSs have been the dominant technology in the market since late 1990s [2]. However, this situation has changed in the recent years with the
development trend of WECSs toward larger power capacity,
lower cost/kilowatt, increased power density, and the need
for higher reliability. More and more attention has been paid
to direct-drive gearless WECS concepts. Among different
types of generators, the permanent-magnet synchronous generators (PMSGs) have been found to be superior owing to
their advantages of higher efficiency, higher power density,

Manuscript received June 1, 2013; accepted August 13, 2013. Date of
publication September 4, 2013; date of current version October 29, 2013.
This work was supported by the U.S. National Science Foundation under Grant
ECCS-0901218. Recommended for publication by Associate Editor Wenzhong
Gao.
The authors are with the Department of Electrical Engineering, University
of Nebraska-Lincoln, Lincoln, NE 68588-0511 USA (e-mail: yue.zhao@
huskers.unl.edu; ; ; wqiao@
engr.unl.edu).
Color versions of one or more of the figures in this paper are available
online at .
Digital Object Identifier 10.1109/JESTPE.2013.2280572

lower maintenance costs, and better grid compatibility [3].
The increased reliability and high performance make
the direct-drive PMSG-based WECSs more attractive in
multimegawatt offshore applications, where the WECSs are

installed in harsh and less-accessible environments [3].
Currently, there are a variety of commercial direct-drive
PMSG-based WECSs in the market, with the power ratings
ranging from hundreds of watts to 6 MW [4], [5]. Many
wind turbine manufacturers, such as Vestas, Siemens Wind
Power, GE Energy, Goldwind, etc. have adopted the directdrive PMSG concept in their WECS products.
The variable-speed WECSs can be operated in the maximum
power point tracking (MPPT) mode to extract the maximum
energy from wind. For this purpose, well-calibrated mechanical sensors, such as anemometers and encoders/resolvers,
are indispensable to acquire the information of wind speed
and generator rotor position/speed. The use of mechanical
sensors, however, increases the cost, hardware complexity, and
failure rate of WECSs [6], [17]. These problems can be solved
by adopting position/speed sensorless control schemes. With
the development of advanced power electronics and microcontroller technologies, position/speed sensorless control for
WECSs becomes feasible. In the literature, a variety of optimal
position/speed sensorless control strategies have been proposed for WECSs with different power electronic converters,
leading to reduced production and maintenance costs, simple
system design, and enhanced system robustness [8], [11]–[14].
Moreover, much research effort in academia and industry has
been devoted to sensorless control methods for motor drives
[7], many of which are potentially applicable to WECSs.
This paper reviews position/speed sensorless control strategies for direct-drive PMSG-based WECSs. Section II reviews
the configurations of the commonly used power electronic
conversion systems in direct-drive PMSG-based WECSs.
Section III reviews the state-of-the-art wind speed sensorless MPPT algorithms for direct-drive PMSG-based WECSs.
In Section IV, the rotor position/speed estimation methods
for vector control of PMSGs are discussed. Section V discusses the application of inherent motion-sensorless direct
torque control (DTC) and direct power control (DPC) for
position/speed sensorless control of direct-drive PMSG-based

WECSs. The challenges and future trends of position/speed
sensorless control for direct-drive PMSG-based WECs are
discussed in Section VI. Section VII concludes this paper.

2168-6777 © 2013 IEEE


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Fig. 1. Schematic diagram of a direct-drive PMSG-based WECS connected
to a grid or local load.

Fig. 3. Direct-drive PMSG-based WECS with a two-level back-to-back PWM
converter system.

Fig. 4. Direct-drive PMSG-based WECS with a three-level NPC back-toback converter system.
Fig. 2. Direct-drive PMSG-based WECS with an MSC consisting of a diode
rectifier and a boost converter.

II. C OMMONLY U SED P OWER E LECTRONIC C ONVERSION
S YSTEMS IN D IRECT-D RIVE PMSG-BASED WECSs
Multipole low-speed PMSGs are the most commonly
used generators in direct-drive variable-speed WECSs [9].
To control the PMSG and regulate the frequency and voltage amplitude of the generated electricity to meet the grid
code compliance requirements, full-scale power electronic
converters are commonly adopted as the interface between
the PMSG-based WECS and the power grid. Fig. 1 shows
the schematic diagram of a typical direct-drive PMSG-based

WECS connected to a grid or local load, where the power
electronic conversion system consists of an ac/dc rectifier, i.e.,
the machine-side converter (MSC), a dc link, and a dc/ac
inverter, i.e., the grid-side inverter (GSI). The MSC is used
to regulate the ac output power of the PMSG with variable
voltage amplitude and frequency into dc power. In addition, the
MSC should have the capability of adjusting the current and
torque of the PMSG to achieve shaft speed, power, or torque
control for the PMSG. The main function of the GSI is to
maintain a constant dc-link voltage, control the reactive power
that the WECS exchanges with the grid, and synchronize the
ac power generated by the WECS with the power grid.
A variety of power converter topologies have been used
in PMSG-based WECSs. In this paper, the sensorless control
schemes are reviewed for the power electronic conversion
systems that have been well developed and widely adopted
by wind turbine manufactures. There are mainly three power
converter topologies in the literature for the MSC: 1) an
uncontrolled diode rectifier cascaded with a boost converter;
2) a fully-controlled two-level pulsewidth modulated (PWM)
rectifier; and 3) a multilevel converter [9]. The WECS with
a diode rectifier and a boost converter, as shown in Fig. 2,
is renowned for its simple structure and low cost [10]. The
magnitude of the regulated dc output voltage of the diode
rectifier is approximately proportional to the rotor speed of the
PMSG [11]. The functions of the boost converter are to step up

and stabilize the dc voltage of the diode rectifier for the GSI
as well as to regulate the rectifier/generator currents for MPPT
control of the WECS. Because the diode rectifier is a naturally

commutated power converter, the voltages and currents of
the PMSG cannot be fully controlled. Therefore, the WECS
equipped with such a converter system inherently does not
need rotor position/speed sensors [12]–[14]. Therefore, the
main issue for sensorless control of this type of WECS is the
MPPT control without wind speed measurements. A detailed
discussion on this aspect will be presented in Section III.
In a direct-drive WECS, if the power electronic converters
consist of fully controllable switching devices, e.g., insulatedgate bipolar transistors and integrated gate-commutated
thyristors, as shown in Fig. 3, the speed, terminal voltage,
and electromagnetic torque of the PMSG can be completely
regulated, leading to improved control flexibility and generation efficiency and reduced torque ripples and current
harmonics [3], [9], [12] when compared with the WECS
in Fig. 2. The cost to achieve these advantages is that the
precise information of the rotor position/speed is needed.
Fig. 3 shows a WECS equipped with two fully rated, twolevel, PWM converters connected back to back via a dc link.
This is the most frequently used power converter topology in
variable-speed WECSs [9]. Fig. 4 shows a WECS with two
three-level, neutral-point clamped (NPC), PWM converters
connected back to back via a dc link. This configuration is
primarily used in medium-voltage and high-power WECSs
[3], [9], [16]. Several manufacturers have released products
based on this power converter topology [12], [15]. The rotor
position/speed sensorless control for the WECSs in Figs. 3
and 4 will be specifically discussed in Sections IV and V.
III. W IND S PEED S ENSORLESS C ONTROL
According to the aerodynamic model of a wind turbine, the
mechanical power Pm captured by the wind turbine from wind
can be expressed as
Pm =

1
ρ Ar v ω3 C p (λ, β)
2

(1)


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Fig. 5. Typical wind turbine torque-shaft speed characteristic curves for
different wind speeds and the OT curve.

where ρ is the air density, Ar is the area swept by the blades,
v ω is the wind speed, C p is the turbine power coefficient, β
is the turbine blade pitch angle, and λ is the tip-speed ratio
(TSR), which is defined by
λ=

ωt R
vω

(2)

where ωt is the turbine shaft speed and R is the radius of
the wind turbine rotor plane. Normally, if β is fixed, there
is an optimal value λopt at which the turbine will extract the
maximum power from wind. The purpose of sensorless MPPT

algorithms is to control the shaft speed of the wind turbine so
as to maintain the optimal TSR without the knowledge of wind
speed.
The existing wind speed sensorless MPPT control methods
can be mainly classified into the following five categories:
1) optimal torque (OT) control; 2) power signal feedback
(PSF) control; 3) perturbation and observation (P&O) control;
4) wind speed estimation (WSE)-based control; and 5) fuzzy
logic (FL) control [18], [19].
A. OT Control
The principle of this method is to adjust the torque of
the PMSG according to an optimal reference torque curve
or lookup table, which is obtained through experimental tests
[20]–[22]. This method has been used in some disclosed
patents of General Electric company for MPPT control of
WECSs [23], [24]. The maximum power that a wind turbine
can extract from wind can be expressed by
Pmax =

R 3 C p max 3
1
ρ Ar
ωt = K opt ωt3
2
λ3opt

(3)

where C pmax is the maximum power coefficient, which is
obtained when the TSR is at the optimal value λopt .

According to Pm = ωt · Tm , the OT Topt of the wind turbine
can be expressed as follows:
Topt =

1
ρ Ar
2

R3 C

p max 2
ωt
3
λopt

= K opt ωt2 .

(4)

The WECS can be operated in the torque control mode with
an optimal reference torque signal obtained from (4) using the

Fig. 6. Typical wind turbine power-shaft speed characteristic curves for
different wind speeds and the optimal power curve.

measured or estimated turbine shaft speed signal. Fig. 5 shows
typical wind turbine torque–shaft speed characteristic curves
and the OT curve for a WECS. Although this control method is
widely used in WECSs because of its simplicity, fast response,
and high efficiency, it requires the information of air density

and turbine mechanical parameters, which vary in different
systems. Moreover, the OT curve, which is mainly obtained
via field tests, will change when the system ages. This will
affect the MPPT efficiency.
B. PSF Control
Fig. 6 shows typical wind turbine power–shaft speed characteristic curves. According to (3), the curve of the maximum
wind turbine power versus shaft speed (i.e., the optimal power
curve) can be obtained and is shown in Fig. 6 as well. Unlike
the OT control, in the PSF control, the turbine shaft speed is
measured or estimated to obtain the optimal power reference
for the MPPT control during operation. Some variations of
this method have been proposed for PMSG-based WECSs.
In [25], the curves of the maximum electrical output power
versus turbine shaft speed were obtained via field tests and
applied for MPPT control of the WECS. In [26], a MPPT
method was proposed for a WECS using a diode rectifier
(Fig. 2). The maximum dc-side electrical power of the diode
rectifier at a given wind speed is proportional to the cube
of the dc-link voltage, and this maximum power versus dc
voltage characteristic was stored in a lookup table for the
real-time MPPT control. A similar method was disclosed in a
patent [27]. In [28], an inverse method was used, in which the
electrical output power was measured, and then the optimal
turbine shaft speed reference was obtained from the optimal
power curve for the MPPT control. The stability analysis
of the PSF control method in [21] was conducted in [29].
It concluded that the PSF control method would provide robust
and cost-effective MPPT control for WECSs.
C. P&O Control
The P&O method, also known as the hill-climb search

(HCS) method, does not require any prior knowledge of the
system and is totally independent of wind speed information
and wind turbine characteristics [30]. Therefore, it has been
widely used in WECSs to search for the MPP [11], [31]–[33].


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During the search process, the command of the generator rotor
speed ω is continuously adjusted by a constant increment or
decrement of dω in each step. This will lead to a variation
of the output electrical power Pe by dPe . If dPe /dω > 0, the
rotor speed keeps increasing and vice versa if dPe /dω < 0.
Obviously, this method works well if the wind speed
changes slowly and the increment dω is small. In real-world
applications, it may, however, fail to reach the MPP under
rapidly changing wind conditions because of the large inertia
of the wind turbines. In addition, it is difficult to choose a
suitable step size because a large step size leads to a faster
response but inevitable oscillation around the MPP, whereas
a small step size improves the MPP accuracy but reduces the
speed of convergence. Moreover, a fast wind speed variation
always causes the rotor speed changing in a wrong direction.
Several advanced HCS-based MPPT control methods have
been proposed for PMSG-based WECSs to improve the efficiency and mitigate or eliminate the aforementioned problems
of the conventional P&O method. In [31], a MPPT control
method was proposed for the WECS shown in Fig. 2. The
MPPT process is based on monitoring the output power of

the generator and then directly adjusting the duty cycle of the
dc–dc boost converter according to the result of comparison
between two successive output power values. The control law
has been implemented based on the following principle:
Dk = Dk−1 +

Dk−1

Fig. 7.

GRBFN-based WSE algorithm.

the optimal power curve corresponding to the current output
power at the step k, and γ is a positive-definite gain.
Some of the methods combined the HCS algorithm and PSF
control. In [33], the data of MPP versus dc-link voltage were
recorded and stored during the training process of an advanced
HCS. Then, the recorded data were used to generate a lookup
table, which was used for fast MPPT execution. In [11], a
P&O method was used to search for the MPPs in the training
mode of operation to obtain the optimal relationship (Idc-opt =
2
K opt Vdc-opt
) of the output dc voltage Vdc-opt and current Idc-opt
of the MSC shown in Fig. 2. Then, the WECS was controlled
based on this optimal relationship.

(5)

Dk−1 = C1 · sgn( Dk−2 ) · sgn(Pin,k−1 − Pin,k−2 )

(6)

where k is the time index, D is the duty cycle, D is the
change of the duty cycle; Pin is the input power value of the
boost converter; C1 is a constant determining the convergence
rate and accuracy of the algorithm, and sgn(·) is the signum
function. This method results in a better exploitation of wind
energy, especially in the low wind speed range. A similar
MPPT method, which adjusts the duty cycle of a power
converter, can be found in [34].
Reference [32] proposed a novel solution to the problems
of the conventional HCS algorithm. It not only improves the
tracking speed and accuracy but also ensures that the HCS
always searches in the correct direction during wind speed
variations. This algorithm assumes that a wind turbine has a
unique optimal power curve, as given by (3). During normal
hill climbing, the K opt in (3) can be determined by measuring
the corresponding output power and rotor speed of the PMSG
when a MPP is detected. Once the value of K opt is obtained,
the optimal power curve will be used as a reference for determining the step size and the direction of the next perturbation.
For example, if the operating point lies on the right to the
optimal power curve, the next perturbation will be decreasing
ω in getting closer to the optimal power curve. In addition, the
step size of perturbation can be determined according to the
distance between the operating point and the optimal power
curve. The control law can be formulated as follows:
∗

d(k + 1) = γ · [ω(k) − ω (k)]

(7)

where d(k + 1) is the duty ratio at step k + 1, ω(k) is
the generator rotor speed at step k, ω∗ (k) is the abscissa of

D. WSE-Based Control
In the traditional TSR control, the generator rotor speed
reference is adjusted to follow the measured variable wind
speed to maintain the TSR expressed by (2) at its optimal
value, so as to ensure the maximum output power from the
wind turbine. In the WSE-based control, the wind speed is
estimated and used to compute the optimal rotor speed command from the optimal TSR [6]. The estimated wind speed can
also be used to compute the optimal power command based
on (1), where the wind turbine mechanical power can be
estimated using the measured electrical output power and
estimated shaft mechanical power losses [35]. The generated
optimal rotor speed/power command is then applied to the
rotor speed/power control loop of the WECS control system.
Different WSE methods have been proposed using the growing neural gas network [36], support-vector regression [37],
backpropagation network [38], Gaussian radial basis function
network (GRBFN) [39], and echo state network [40].
Fig. 7 shows a three-layer GRBFN used to provide a static
nonlinear inverse mapping of the wind turbine aerodynamic
model (1) to estimate the wind speed. The overall input–output
mapping for the GRBFN is given by
h

vˆw = b +

v j exp −
j =1

x − Cj
σ j2

2

(8)

where x = [Pm , ωt , β] is the input vector; C j R n and σ j R
are the center and width of the j th RBF unit in the hidden
layer, respectively, h is the number of RBF units, b and v j are
the bias term and weight between the hidden and output layers,
respectively, and vˆw is the estimated wind speed. The turbine


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TABLE I
C OMPARISON OF D IFFERENT W IND S PEED S ENSORLESS MPPT A LGORITHMS [9], [16], [17]

ωe

mechanical power Pm can be estimated from the measured
electrical output power; the turbine shaft speed ωt can be either
measured using sensors or estimated; the blade pitch angle β
always remains constant in the MPPT region. The parameters

of the GRBFN are determined by an offline training process
using a training data set generated from the WECS dynamic
characteristics [6], [63] or an online training process using the
data generated from a P&O method [65]. Once trained, the
parameters of the GRBFN are then fixed for real-time WSE.

θ re

Fig. 8.

E. FL Control
The FL control has been proved to be effective in MPPT
applications without the knowledge of wind turbine characteristics and wind speed [41]–[44]. It has the advantages of
a universal control algorithm, fast convergence, insensitive
parameters, and good immunity to noise and inaccurate signals [41]. The fast convergence is achieved by adaptively
decreasing the step size during the search process. With the
information of the increment/decrement of the generator rotor
speed ω∗ , the corresponding increment/decrement of the
electrical output power Pe of the generator is estimated.
If both Pe and ω∗ are positive, the search continues in the
same direction. On the other hand, if a positive ω∗ leads to
a negative Pe , the direction of search reverses. The Pe and
ω∗ in the current step and ω∗ in the last step are described
by triangular membership functions in the fuzzification stage,
and then, a control law is produced based on a rule table,
which finally generates a generator speed command signal
after defuzzification. In [42], a Takagi–Sugeno–Kang (TSK)
fuzzy model was designed for wind speed sensorless MPPT
based on a combination of a fuzzy clustering method, a genetic
algorithm, and a recursive least-square optimization method.

The TSK fuzzy controller uses the measured rotor speed and
electrical output power of the generator as two inputs and
outputs the maximum power command signal. This model
has a high computational speed, low memory occupancy, and
learning and fault-tolerance capability.
Table I compares the wind speed sensorless MPPT control
algorithms discussed in this section.
IV. ROTOR P OSITION /S PEED S ENSORLESS V ECTOR
C ONTROL FOR PMSGs

Definitions of coordinate reference frames for PMSG modeling.

these reference frames are shown in Fig. 8. The dynamical
model of a generic three-phase PMSG can be written in the
synchronously rotating dq reference frame as follows:
⎧
d
⎪
⎨ v d = Rs i d + L d i d − ωe L q i q
dt
(9)
⎪
⎩ v q = Rs i q + L q d i q + ωe L d i d + ωe ψm
dt
where v q and v d are the q- and d-axis stator terminal voltages, respectively, i q and i d are the q- and d-axis stator
currents, respectively, L q and L d are the q- and d-axis
inductances, respectively, ψm is the flux linkage generated
by the permanent magnets, Rs is the resistance of the stator
windings, and ωe is the electrical angular velocity of the rotor.
The q- and d-axis flux linkages of the PMSG, ψq and ψd ,

can be expressed as follows:
ψd = L d i d + ψm
ψq = L q i q .

The electromagnetic torque Te can be calculated by
3
(11)
Te = po ψm i q + (L d − L q )i d i q
2
where po is the number of pole pairs. The output electrical
power can be calculated by
3
Pe = (v q i q + v d i d ).
(12)
2
Using the inverse Park transformation, the dynamics of the
PMSG can be modeled in the αβ stationary reference frame
as follows:
d iα
vα
L sin θre
L + L cos(2θre )
=
·
vβ
L sin θre
L − L cos(2θre )
dt i β

A. Modeling of PMSGs and Problem Description

A PMSG can be modeled using phase abc quantities.
Through proper coordinate transformations, the PMSG models
in the dq rotating reference frame and the αβ stationary
reference frame can be obtained. The relationships among

(10)

+Rs

iα
iβ

+ K e · ωe ·

− sin θre
cos θre

(13)

where θre is the rotor position angle, v α and v β are the
α- and β-axis stator voltages, respectively, i α and i β are the


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Position/speed estimation schemes for PMSGs based on fundamental-frequency model.

α- and β-axis stator currents, respectively, K e is the back
electromotive force (EMF) constant, L = (L d + L q )/2, and
L = (L d − L q )/2. If the saliency of the PMSG can be
ignored, i.e., L d = L q , then L will be zero, and (13) can be
further simplified as follows:
⎧
d
⎪
⎨ v α = Rs i α + L i α − K e ωe sin θre
dt
(14)
⎪
⎩ v = R i + L d i + K ω cos θ .
β
s β
β
e e
re
dt
If the rotor saliency, however, cannot be ignored, i.e.,
L d = L q , the dynamic analysis performed in the αβ stationary
reference frame using (13) will be complex.
The dynamic model of a PMSG in the dq reference
frame rotating synchronously with the rotor magnet flux
can be expressed as (9), which shows how to control the
current components by means of the applied voltage components through a vector control scheme for a PMSG-based
WECS equipped with an active rectifier (Figs. 3 and 4).
The reference values for the rectifier ac-side voltage vector
can be generated using two independent proportional-integral

(PI) current controllers with feedforward voltage compensation [45]. The vector control requires the measurements of
the stator currents, dc-bus voltage, and rotor position [46].
In the conventional vector control for PMSGs, the rotor
position is measured by electromechanical or optical position
sensors. The use of these sensors, however, increases the
cost, size, weight, and hardware wiring complexity of the
PMSG vector control system. From the viewpoint of system
reliability, mounting position sensors on rotor shafts will
degrade mechanical robustness of the PMSGs. As for WECS
applications, because low-cost, reliable, and compact systems
are always desired, the elimination of position/speed sensors is
desired.
During the last decades, to overcome the drawbacks of
sensor-based motor drives, much research effort has gone into
the development of sensorless motor drives that have comparable dynamic performance with sensor-based motor drives.
Many position sensorless control schemes have been developed for permanent-magnet synchronous machines (PMSMs)

used in applications such as electric-drive vehicles, home
appliances, and etc. Although little work has been reported on
position sensorless vector control for PMSG-based WECSs,
the methods developed for other industrial sensorless PMSM
drives can be well transferred into the PMSG-based WECS
applications. Position sensorless vector control for the PMSGs
used in direct-drive WECSs could be easier than those in
other industrial applications because of several factors. First,
the difference between the d- and q-axis inductances of
the PMSGs used in direct-drive WECSs is usually small.
Sensorless control of a nonsalient-pole PMSG is much easier
than that of a PMSM with large saliency in the medium- and
high-speed range. Second, the operating ranges for the PMSGs

used in WECSs are relatively limited and rarely reach the
flux-weakening region. Moreover, different from the PMSM
applications, such as traction motors in electric-drive vehicles,
in a WECS, the rotating speed of the PMSG is usually
relatively stable and a large abrupt torque/speed change rarely
happens.
This section reviews rotor position/speed estimation
schemes applicable for PMSG vector control systems. Some
of these schemes have already been investigated for position
sensorless control of PMSG-based WECSs. Considering the
operating range of a PMSG-based WECS, e.g., no power
generation below the cut-in wind speed, this review mainly
focuses on the medium- and high-speed ranges. In this speed
region, the methods based on the fundamental-frequency
PMSG models are commonly used for rotor position and
speed estimation. Those methods can be generally grouped
into two categories: 1) open-loop calculation and 2) closedloop observers, as shown in Fig. 9. Per previous discussion,
the difference between d- and q-axis inductances is small for
the PMSGs in wind applications. Therefore, in this section,
rotor position/speed estimation methods are discussed based
on (14). In recent years, salient-pole PMSMs, e.g., interior
PMSMs (IPMSMs) [47], [48], were also proposed for wind
applications. Using reconstructed machine models, e.g., the
extended EMF model [49] and the active flux model [50],
a salient-pole PMSM model can be converted into a model
similar to a nonsalient-pole PMSM. Therefore, the methods


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discussed in this section can also be applied to salient-pole
PMSMs.

L sa =

B. Open-Loop Calculation
The open-loop rotor position/speed estimation methods
behave like real-time dynamic models of the PMSGs to receive
the same control inputs as for the PMSGs and run in parallel
with the PMSGs. With the dynamic model of a PMSG, the
states of interest, e.g., EMF, flux, and winding inductance,
can be calculated, from which the rotor position and speed
information can be extracted.
1) Flux Linkage-Based Methods [51], [52]: At steady state
where diα /dt ≈ 0 and diβ /dt ≈ 0, the stator and rotor flux
vectors rotate synchronously. Therefore, if the position angle
of the stator flux can be calculated, the rotor flux angle can
also be determined, which is the same as the rotor position
angle. According to (14), the voltage and current components
in the stationary reference frame can be used to compute the
stator and rotor flux linkages as follows:
ψsα =
ψsβ =

a-phase resistance, and ea is the a-phase back EMF. According
to (16), L sa can be calculated as follows:

(v α − Rs i α )dt

v β − Rs i β dt

and
ψrα = ψsα − Li α
ψrβ = ψsβ − Li β

(15)

where ψsα and ψsβ are the α- and β-axis stator flux linkages,
respectively, and ψrα and ψrβ are the α- and β-axis rotor
flux linkages, respectively. Then, the rotor position can be
calculated as θre = tan−1 (ψrβ /ψrα ). The accuracy of the fluxbased methods highly depends on the quality and accuracy of
the voltage and current measurements. Since integrators are
needed in this method, the initial condition of the integration
and integration drift are the problems that should be properly
handled. In addition, this method may work well in the steady
state, but the transient performance is usually unsatisfactory.
Similar methods, called flux observers, were proposed in [53]
and [54] for position sensorless control of PMSGs in WECSs,
where phase-locked loops (PLLs) were used to extract the
position information from the estimated rotor flux.
2) Inductance-Based Methods [56]: The basic idea of this
type of methods is that the spatial distribution of the phase
inductance of a PMSG, especially the PMSG with a high
saliency ratio, is a function of the rotor position. The phase
inductance can be calculated from the measured voltages and
currents. Then, the rotor position can be estimated from the
calculated phase inductance using a lookup table. In a PMSG
control system, if the switching frequency is high enough, the
values of the phase inductance and back EMF can be viewed

as constant during a switching period. Under this assumption,
the dynamic voltage equation for phase a of a PMSG can be
expressed as follows:
v a = Ra i a + L sa

di a
+ ea
dt

(16)

where v a is the a-phase terminal voltage, i a is the a-phase
current, L sa is the a-phase synchronous inductance, Ra is the

v a − R a i a − ea
di a /dt

(17)

where the instantaneous value of the back EMF ea can be
evaluated using the calculated rotor position in the previous
two control cycles, i.e., ea (k) = K e · [θre (k) − θre (k−1)]/ t.
According to the phase inductance obtained by (17), the rotor
position can be obtained from a lookup table that was created
to store the relationship between the rotor position and phase
inductance. The accuracy of the inductance-based methods
also highly depends on the quality and accuracy of the voltage
and current measurements. Since the current and position
derivatives need to be calculated in every switching cycle, the
rotor position is highly subjected to the measurement noise.

In addition, this type of methods requires that the PMSG has
a high saliency ratio, e.g., L q /L d > 2.5, and the performance
will be poor for nonsalient-pole PMSGs.
3) Algebraic Manipulation [57]: The basic idea of this
method is to solve a set of equations formed by the PMSG
model and coordinate transformations, because the rotor
position can be expressed in terms of PMSG parameters
and measured currents and voltages. Specifically, the following coordinate transformations are required by this method:
the Park transformations for the PMSG currents (18a) and
voltages (18b)
i d = i α cos θre + i β sin θre
i q = −i α sin θre + i β cos θre

(18a)

v d = v α cos θre + v β sin θre
v q = −v α sin θre + v β cos θre

(18b)

and the Clarke transformations for the PMSG currents (18c)
and voltages (18d)
iα = ia √
√
i β = −i b
3 + ic
3

(18c)

vα = va √
√
v β = −v b
3 + vc
3.

(18d)

By manipulating (18) and PMSG equations (9), the rotor
position can be calculated as follows:
θre = tan−1
√
b−ic )
− 3ωe (L d − L q )i a
v b −v c − Rs (i b −i c )− L d d(idt
√
3 v a − Rs i a − L d didta +ωe (L d − L q )(i b −i c )

.
(19)

The accuracy of this method is also strongly dependent on
the accuracy of PMSG parameters and quality and accuracy
of voltage and current measurements. Since current derivatives
also need to be calculated in every switching cycle, the rotor
position is highly subjected to the measurement noise.
In conclusion, the open-loop calculation-based PMSG rotor
position estimation methods are straightforward and easy to
implement. The resolution of the rotor position obtained from
these methods is, however, limited by the numerical resolution,

which depends on the sampling frequency and control-loop
frequency of the system. The accuracy of these methods is


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for the observer to achieve a desired tracking performance.
With the estimated back EMF, the rotor position can be
obtained by
eˆα
.
(22)
θˆre = tan−1 −
eˆβ

Fig. 10. Illustration of (a) linear observer, e.g., a disturbance observer, and
(b) SMO for back EMF estimation of a PMSG.

strongly dependent on the accuracy of machine parameters
and voltage and current measurements. These methods are
still useful, but may need to be improved using closed-loop
observers discussed in the following section.
C. Closed-Loop Observers
In a closed-loop observer, both the control input of the
plant and the tracking error of the observer, i.e., the error
between plant and observer outputs, are often used as the input
signals to the observer. The observer gains are designed in
forcing the observer output to converge to the plant output.

Thus, the estimated values for the states of interest can be
forced to converge to their actual values. In this sense, the
closed-loop observer can be viewed as an adaptive filter, which
has good disturbance rejection property and good robustness
to the variations of PMSG parameters and current/voltage
measurements. In the literature, many observers have been
proposed for rotor position/speed estimation, such as disturbance observers, sliding-mode observers (SMO), and extended
Kalman filters (EKFs).
1) Disturbance Observers: The EMF or extended EMF can
be estimated using disturbance voltage observers, as shown
in Fig. 10(a), in which the EMF is regarded as a kind of
disturbance voltage. These observers were usually designed
based on the dynamic models of PMSGs in the αβ stationary
reference frame. From (14), the state-space equations of a
PMSG can be express as follows:
1
d iα
0
−Rs /L
i
=
· α + ·
iβ
0
−Rs /L
dt i β
L

vα
vβ

−

eα
eβ
(20)

where e = [eα ,eβ ]T = [−K e ωe sin(θre ), K e ωe cos(θre )]T is the
vector of EMF terms. In [58], based on the assumption that
de/dt ≈ 0, a disturbance observer was designed as follows:
d
dt

1
0
iˆα
−Rs /L
i
=
· α + ·
iβ
0
−Rs /L
L
iˆβ

vα
vβ

−

eˆα
eˆβ

and
d
dt

eˆα
eˆβ

=g·

d
dt

iˆα − i α
iˆβ − i β

(21)

where ^ denotes the estimated values and g is the observer
gain, which can be designed using a pole assignment scheme

A disturbance observer was also proposed in [49] for
IPMSM applications based on the extended EMF model in
the αβ stationary reference frame. A similar observer design
was proposed in [59] based on the extended EMF model in an
estimated dq reference frame. The stability of a disturbance
observer can be guaranteed by selecting proper observer gains.

Because machine parameters are needed in the observers’
models, the variations of those parameters will slightly affect
the accuracy of the position estimation, especially when both
the d- and q-axis inductances have cross saturation. In addition, the quality of voltage and current measurements could
also affect the performance of disturbance observers. A similar
disturbance observer can be found in the PMSG wind turbine
control system in [60].
2) Sliding-Mode Observers: An SMO is an observer whose
inputs are discontinuous functions of the errors between the
estimated and measured system states. If a sliding manifold
is well designed, when the trajectories for the states of
interest reach the designed manifold, the sliding mode will
be enforced. The dynamics for the states of interest under
the sliding mode depend only on the manifold chosen in
the state space and are not affected by system structure or
parameter accuracy. Advantages such as high robustness to
system structure and parameter variations make the SMO a
promising solution for sensorless control of PMSMs. Still
using (14) to model a PMSG, an SMO [61] [Fig. 10(b)] was
designed as follows:
d
dt

1 vα
0
iˆα
−Rs /L
iˆ
=
· α +

ˆi β
ˆ
0
−Rs /L
L vβ
iβ
ˆ
ωc
i − iα
· k · sgn α
− 1+l
s + ωc
iˆβ − i β

(23)

where ωc is the cutoff frequency of the low-pass filter (LPF);
l is the observer feedback gain; and k is the gain of the
switching terms. In this case, the sliding surface is designed
T
as S = iˆα − i α , iˆβ − i β . By properly selecting l and k,
V = 1/2·S T · S > 0 and d V /dt < 0 can be guaranteed, so as
the observer stability. If the sliding mode is enforced, the back
EMF components can be estimated by
eˆα
eˆβ

= k (1 + l) ·

ωc

iˆ − i α
.
· sgn α
ˆ
s + ωc
iβ − iβ .

(24)

Then, the rotor position can be calculated using (22). Many
variations of (23) can be found in the literature, e.g., using a
saturation function or a sigmoid function to replace the sgn
function to mitigate the chattering problem. The design of the
sliding surface can also be different. In addition, several online
adaption schemes [62] have also been proposed to improve the
observer robustness to machine parameter variations. A similar
SMO was designed based on the extended EMF model for
IPMSM applications [63].


ZHAO et al.: REVIEW ON POSITION/SPEED SENSORLESS CONTROL

211

where
xˆ =

xˆ1
xˆ2

Aˆ =

−Rs /L d L q ωˆ e /L d
.
−L d ωˆ e /L q −Rs /L q

The adjustable model uses the estimated speed to correct the
estimation of the matrix A. The adaptive mechanism for rotor
speed update can be expressed as follows:
Fig. 11.

Schematic diagram of a MRAS-based speed estimator.

t

ωˆ e =

k1 i d iˆq − i q iˆd − ψm i q − iˆq

L d dτ

0

However, in practical applications, the attractive features
of the SMO, such as robustness to machine parameter and
load variations, will degrade if the system has a low sampling
ratio and control-loop frequency. As discussed in [64], the
performance of the SMO without oversampling is much worse
than the case with oversampling. A solution to this problem
is the quasi-SMO [63] with a discretized convergence law.

Compared with the disturbance observers, which are the examples of linear state observers using continuous-state feedback,
the SMO is a representative of nonlinear observers using the
output of a discontinuous switching control as the feedback. If
the gains of the switching functions are tuned well, the SMO
will have better dynamic performance than the disturbance
observers. Well-designed LPFs are, however, needed in the
SMO to mitigate the oscillating position errors due to the
unwanted noise caused by switching functions. As an attractive
candidate for position sensorless control, the SMO has also
been applied to PMSGs for wind applications [65], [66].
3) Model Reference Adaptive System-Based Methods:
The model reference adaptive system (MRAS) is an effective scheme for speed estimation in motor drives. In a
MRAS, as shown in Fig. 11, an adjustable model and a
reference model are connected in parallel. The output of
the adjustable model is expected to converge to the output
of the reference model using a proper adaption mechanism.
If the output of the adjustable model tracks that of the
reference model accurately, the internal states of these two
models should be identical. In [67], the reference model was
formulated as follows:
d
x = A·x+u
dt

(25)

where
x=

x1

x2

=

i d + ψm L d
iq

u=

u1
u2

=

(v d L d + ψm ) L 2d
vq L q

A=

−Rs L d L q ωe L d
.
−L d ωe L q −Rs L q

The adjustable model was defined as
d
xˆ = Aˆ · xˆ + u
dt

(26)

+k2 i d iˆq − i q iˆd − ψm i q − iˆq

L d + ωe (0) . (27)

The stability of the MRAS and convergence of the speed
estimation can be guaranteed by the Popov super stability
theory [67]. Per previous discussion, if the tracking errors
between the states of the adjustable and reference models are
close to zero, the estimated speed in (27) can be viewed as
the actual speed. Then, the rotor position can be obtained by
integrating the estimated rotor speed. There are other options
for designing the reference model. For example, a disturbance
observer and an SMO were used as the reference model in
[49] and [68], respectively, and the corresponding adaptive
mechanisms for rotor speed adaption are also different.
4) EKF-Based Methods: As an extension of the Kalman
filter, which is a stochastic state observer in the least-square
sense, the EKF is a viable candidate for online estimation of
rotor position and speed of a PMSM. In the EKF algorithm, the
system state variables can be selected in either a rotating [69]
or a stationary [70] reference frame, i.e., x = [i d , i q , ωe , θre ]T
and x = [i α , i β , ωe , θre ]T , respectively. A standard EKF
algorithm contains three steps: a) prediction; b) innovation;
and c) Kalman gain update. Due to the stochastic properties
of the EKF, it has great advantages in robustness to measurement noise and parameter inaccuracy. However, tuning
the covariance matrices of the model and measurement noise
is difficult [69]. In addition, the EKF-based algorithms are
computationally intensive and time consuming, which makes
the EKF hard to be implemented in industrial drives.
D. Position/Speed Extraction Methods

Per the review in Sections IV-B and C, by selecting a suitable method, position/speed related states, such as back EMF
or flux, can be estimated. Then, appropriate position/speed
extraction methods are needed to obtain the rotor position
and speed information from these estimated states. If the two
orthogonal back EMF components eˆα and eˆβ are obtained,
the simplest and most straightforward method to calculate the
rotor position is using (22). However, this method is an openloop method, which is quite sensitive to the input noise. In
addition, if the output of the observer is a position error signal,
which is a function of the difference between the estimated and
actual rotor positions, (22) cannot be used.
In addition to (22), the PLL-based and angle tracking
observers are also effective methods. Many applications of
these methods can be found in the literature for rotor position
extraction in PMSG-based WECS control systems [54], [55].


212

Fig. 12.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 1, NO. 4, DECEMBER 2013

Block diagram for a PLL-based position extraction method.
Fig. 13.

A typical PLL-based position extraction method is shown in
Fig. 12, where Msinθre and Mcosθre are the orthogonal input
signals, e.g., the estimated EMF components, where M is the
amplitude. If the difference between the estimated and actual
positions is small, the following relationship can be obtained

Msin θre cos θˆre − Mcos θre sin θˆre = Msin θre − θˆre ≈ M θ.
(28)
A PI regulator can be used to estimate the rotor speed from
M θ . Then, the rotor position can be obtained using a speed
integrator. The transfer function of the PLL can be expressed
as follows:
k p s + ki
θˆre
= 2
.
(29)
θre
s + k p s + ki
The dynamic behavior of (29) depends on the PI gains,
which can be determined by appropriately placing the poles
of the characteristic polynomial of the transfer function. If the
output of the state observer is already a function of θ , it can
be used directly by the PLL as an equivalent term to M θ .
The rotor speed can be obtained directly from a MRASbased method, such as (27), or a PLL method. Alternatively,
rotor speed can be simply and effectively estimated from rotor
position using a moving average algorithm. However, due to
the time-delay properties of the moving average algorithm, a
phase lag will exist between the estimated and actual rotor
speeds. To mitigate this issue, a torque feedforward based
speed correction can be used [71].
V. DTC AND DPC FOR D IRECT-D RIVE
PMSG-BASED WECSs
A. Direct Torque Control
As a promising control scheme, the DTC is primarily
developed for high-performance electric motor drive systems.

In contrast to the vector control, the DTC adopts the electromagnetic torque and stator flux linkage as the control
variables. It not only achieves a faster torque response but
also avoids the coordinate transformations and current decoupling computation in the vector control. Therefore, the DTC
is an inherent motion-sensorless control strategy [72]. ABB
successfully launched the first commercial DTC drive product
in 1995 [73], and then DTC wind turbine converters joined
ABB’s product family [74]. For WECS applications, one
advantage of adopting the DTC is that the outer speed control
loop in the vector control can be eliminated using a torquecommand sensorless MPPT algorithm [75]. The schematic

Schematic diagram of a DTC for a PMSG-based WECS.

diagram of the DTC for a PMSG-based WECS is shown in
Fig. 13. At each sampling instant, according to the differences
between the reference and actual torques and stator flux
linkages, an optimal stator voltage vector will be selected
directly from a switching table to restrain the torque and
flux within the hysteresis bands. However, due to the use of
the hysteresis comparators and discrete-time controllers, the
unpredictable torque and current ripples of the PMSG cannot
be neglected. This drawback may increase the mechanical
stress on the turbine shaft, reduce the turbine life, and produce
much acoustic noise [9]. Using a three-level NPC converter
instead of a two-level converter may improve the performance
of the DTC under stead-state operation [76]. An alternative
is to integrate the space–vector modulation (SVM) into the
DTC [66], [75]. However, according to the characteristics of
the DTC [72], the modified SVM-DTC cannot be considered
a genuine DTC scheme.
B. Direct Power Control

The DPC, which follows the idea of the DTC, also gains
the advantages such as fast dynamic response, no coordination
transformation, simple implementation, and high robustness
to parameter variations [77], [78]. Compared with the DTC,
the DPC is used to control the GSI instead of the MSC
and the control variables in the DPC are instantaneous active
power and reactive power, which makes the DPC suitable and
promising for either generation control or grid connection in
microgrid applications [79].
The DPC was initially proposed for three-phase PWM rectifiers and then was naturally adopted for DFIG-based WECSs
[80]. Nevertheless, there are few studies on the DPC for directdrive PMSG-based WECSs reported in the literature. The
schematic diagram of the conventional DPC for a GSI is shown
in Fig. 14. Similar to the DTC, the main drawbacks of the DPC
are high power and current ripples. The performance of a DPC
would become worse when the operating points are close to
the power limits of the GSI [79]. To mitigate the weakness
of the DPC, some trials have been devoted to improving and
optimizing the switching table [78], [79], [81]. Some research
has been conducted to lower the total harmonic distortion
(THD) and achieve a fixed switching frequency using PI
regulators integrated with a space–vector PWM scheme [82],
or a model-based predictive control instead of the hysteresis
controllers [83]. In [84], the DPC was applied for controlling a
three-level GSI with inductor-capacitor-inductor filters for the


ZHAO et al.: REVIEW ON POSITION/SPEED SENSORLESS CONTROL

Fig. 14.

Schematic diagram of a DPC for a PMSG-based WECS.

application to a PMSG-based WECS, in which an uncontrolled
diode rectifier and a dc–dc boost converter were adopted to
form the MSC. Therefore, the overall control system of the
PMSG and the power electronic converters can be treated as a
rotor position/speed sensorless control scheme. The use of PI
controllers and space–vector PWM indeed reduces the power
and current ripples, but the merits of simple implementation
and fast dynamic response in the conventional DPC are lost.
VI. C HALLENGES AND F UTURE T RENDS
From the review and discussion in the previous three sections, it can be seen that the position/speed sensorless control
technology for direct-drive PMSG-based WECSs has been
extensively studied with a surge of interests being prompted
by the availability of more powerful digital signal processing
devices. However, despite the aforementioned developments,
there are still challenges, which limit the applications of
some of the aforementioned sensorless control algorithms in
real-world WECSs. Significant research effort is desired to
overcome the challenges.
Among the existing wind speed sensorless MPPT techniques, the OT and PSF are the most mature ones. The
implementation of the OT and PSF techniques, however,
requires prior knowledge of the WECSs. Moreover, the accuracy of these MPPT methods is affected by system parameter
variations caused by the aging of the WECSs. Compared
with the OT and PSF methods, the advanced HCS method is
independent of system characteristics, but has slower tracking
speed and lower robustness to disturbances and abrupt changes
in wind speed conditions. Further studies can be carried out
to combine the existing methods, such as the combination of
HCS and PSF or HCS and OT, which will enable the WECS

to learn the MPPs online without the need of field tests and
switch to the fast and smooth MPPT control after the MPPs
are learned. Moreover, advanced wind speed sensorless MPPT
algorithms based on computational intelligence (e.g., artificial
neural networks, FL, etc.) are capable of fast online learning
of MPPs and adaption to system dynamic characteristics
variations caused by the aging of the WECSs. The applications
of the computational intelligence-based methods are, however,
currently limited by their relatively high computational costs.
With the availability and cost reduction of higher performance
processors, these methods will be good options for efficiency
optimization and performance improvement of WECSs.
There are two major challenges in designing rotor position/speed sensorless control algorithms for the PMSGs used

213

in the drive–drive WECSs. First, the operating conditions of
a WECS always change from time to time because of the
intermittent nature of wind energy. This requires the sensorless
control algorithms to achieve satisfactory steady-state and
dynamic rotor position/speed estimation precision over the
entire speed range of the WECS. This requirement, however,
cannot be met by a single sensorless control technique [85] and
may need different control techniques for different operating
conditions. In addition, because a WECS is connected to a
power grid or feeds a load as a stand-alone system, the performance of the sensorless control algorithms under abnormal
or fault conditions, e.g., grid/load unbalance or short circuits,
should be examined and evaluated. To the best of the authors’
knowledge, few studies have been done on these subjects for
sensorless control in the literature. Therefore, more research

needs to be launched to overcome these two major challenges
in the rotor position/speed sensorless control of the PMSGbased WECSs.
The application of the inherent motion-sensorless DTC and
DPC in PMSG-based WECSs is promising and attractive.
Further studies are, however, needed to solve the problems
such as irregular torque, power and current ripples, and high
THD.
VII. C ONCLUSION
The demand of highly reliable wind power with lower
production and maintenance costs will make position/speed
sensorless control a promising technique in WECS applications. This paper has reviewed the most recent progress in the
field of position/speed sensorless control schemes for directdrive PMSG-based WECSs. First, the mainstream power
electronic converter topologies used in direct-drive PMSGbased WECSs were reviewed. Then, the existing wind speed
sensorless MPPT methods, including the OT control, PSF
control, P&O control, WSE-based control, and FL control,
were discussed. To the best of the authors’ knowledge, the
most commonly used wind speed sensorless MPPT methods
are the OT and PSF controls, depending on the selection of
the control commands. These wind speed sensorless MPPT
methods generate the optimal rotor speed, torque or power
references for the control systems of the PMSGs, which can
then use either a rotor position/speed estimation-based sensorless vector control or direct torque or direct power control to
track the MPPs of the WECSs without using generator rotor
position/speed sensors. In the fully controlled rectifier-based
PMSG WECSs, the SMO is a good candidate for the rotor
position estimation used in the vector control system. The
inherent motion-sensorless DTC and DPC provide promising
and attractive alternatives for position/speed sensorless control
of PMSG-based WECSs.
When applying position/speed sensorless control algorithms

to direct-drive PMSG-based WECSs, various critical issues,
such as stability, reliability, robustness, and complexity of the
algorithm and system cost, should be carefully considered and
judged. However, it is the belief of the authors that with the
further development of the sensorless control techniques and
reduction in the cost of processors, the sensorless-controlled


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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 1, NO. 4, DECEMBER 2013

direct-drive PMSG-based WECSs will become technically feasible in the near future and, therefore, enhancing the reliability
and economic viability of the WECSs.
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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 1, NO. 4, DECEMBER 2013

Yue Zhao (S’10) received the B.S. degree in electrical engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2010.
He is currently pursuing the Ph.D. degree in electrical engineering from the University of NebraskaLincoln, Lincoln, NE, USA.
He was a Graduate Student Researcher in 2011 and
2012 and a summer Product Engineering Intern in
2013 with John Deere Electronic Solutions, Fargo,
ND, USA. His current research interests include
electric machines and drives, power electronics, and
control.
Mr. Zhao is a member of Eta Kappa Nu. He was a recipient of the Best
Paper Prize of the 2012 IEEE Transportation Electrification Conference and
Expo.

Chun Wei (S’12) received the B.S. degree in electrical engineering from Beijing Jiaotong University,
Beijing, China, in 2009, and the M.S. degree in
electrical engineering from North China Electric
Power University, Beijing, China, in 2012. He is
currently pursuing the Ph.D. degree in electrical

engineering at the University of Nebraska-Lincoln,
Lincoln, NE, USA.
His current research interests include wind power
technology, power electronics, and renewable energy
systems.

Zhe Zhang (S’10) received the B.S. degree in electrical engineering from Xi’an Jiaotong University,
Xi’an, China, in 2010. He is currently pursuing
the Ph.D. degree in electrical engineering from the
University of Nebraska-Lincoln, Lincoln, NE, USA.
His current research interests include control of
wind energy conversion systems, power electronics,
and motor drives.

Wei Qiao (S’05–M’08–SM’12) received the B.Eng.
and M.Eng. degrees in electrical engineering from
Zhejiang University, Hangzhou, China, in 1997 and
2002, respectively, the M.S. degree in high performance computation for engineered systems from
Singapore-MIT Alliance, Singapore, in 2003, and
the Ph.D. degree in electrical engineering from Georgia Institute of Technology, Atlanta, GA, USA, in
2008.
He has been with the University of NebraskaLincoln (UNL), Lincoln, NE, USA, since August
2008, where he is currently an Associate Professor in the Department of
Electrical Engineering. His current research interests include renewable energy
systems, smart grids, microgrids, condition monitoring and fault diagnosis,
energy storage systems, power electronics, electric machines and drives, and
computational intelligence for electric power and energy systems. He is the
author or coauthor of three book chapters and more than 120 papers in referred
journals and international conference proceedings.
Dr. Qiao is an Associated Editor of the IEEE T RANSACTIONS ON I NDUS TRY A PPLICATIONS , an Associate Editor of the IEEE J OURNAL OF E MERG ING AND S ELECTED T OPICS IN P OWER E LECTRONICS , and the Chair of the

Sustainable Energy Sources Technical Thrust of the IEEE Power Electronics
Society (PELS). He is the Publications Chair of the 2013 IEEE Energy
Conversion Congress and Exposition. He was the Technical Program Co-Chair
and Publications Chair of the 2012 IEEE Symposium on Power Electronics
and Machines in Wind Applications (PEMWA) and the Technical Program
Co-Chair and Finance Co-Chair of PEMWA 2009. He was the recipient of a
2010 National Science Foundation CAREER Award, the 2010 IEEE Industry
Applications Society (IAS) Andrew W. Smith Outstanding Young Member
Award, the 2012 UNL College of Engineering Faculty Research & Creative
Activity Award, the 2011 UNL Harold and Esther Edgerton Junior Faculty
Award, and the 2011 UNL College of Engineering Edgerton Innovation
Award. He has received four Best Paper Awards from IEEE IAS, PES, and
PELS.