A Fuzzy Logic Control Strategy for Doubly Fed Induction Generator for Improved Performance under Faulty Operating Conditions

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 4, No. 4, December 2014, pp. 419~429
ISSN: 2088-8694  419
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
A Fuzzy Logic Control Strategy for Doubly Fed Induction
Generator for Improved Performance under Faulty Operating
Conditions
G. Venu Madhav*, Y. P. Obulesu**
* Department of Electrical and Electronics Engineering, Padmasri Dr. B. V. Raju Institute of Technology
** Department of Electrical and Electronics Engineering, LakiReddy BaliReddy College of Engineering
Article Info ABSTRACT
Article history:
Received Mar 12, 2014
Revised May 21, 2014
Accepted Jun 20, 2014
In this paper, decouple PI control for output active and reactive powers
which is the common control technique for power converter of Doubly Fed
Induction Generator (DFIG) is presented. But there are some disadvantages
with this control method like uncertainty about the exact model, behavior of
some parameters or unpredictable wind speed and tuning of PI parameters.
To overcome the mentioned disadvantages a fuzzy logic control of DFIG
wind turbine is presented and is compared with PI controller. To validate the
proposed scheme, simulation results are presented, these results showed that
the performance of fuzzy control of DFIG is excellent and it improves power
quality and stability of wind turbine compared to PI controller. The Fuzzy
logic controller is applied to rotor side converter for active power control and
voltage regulation of wind turbine. The entire work is carried out in
MATLab/Simulink. Different faulty operating conditions are considered to
prove the effective implementation of the proposed control scheme.
Keyword:
Wind turbine
Doubly fed induction generator
Fuzzy logic control
PI controller
Synchronous generator
Copyright © 2014 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
G. Venu Madhav,
Department of Electrical and Electronics Engineering,
Padmasri Dr. B. V. Raju Institute of Technology,
Vishnupur, Narsapur, Medak Dist., 502313, AP, India.
Email: venumadhav.gopala@gmail.com
1. INTRODUCTION
Wind energy is one of the extra ordinary sources of renewable energy due to its clean character and
free availability. Moreover, because of reducing the cost and improving techniques, the growth of wind
energy in Distributed Generation (DG) units has developed rapidly.
In terms of wind power generation technology, because of numerous technical benefits (higher energy yield,
reducing power fluctuations and improving var supply) the modern MW-size wind turbines always use
variable speed operation which is achieved by a converter system [1]. These converters are typically
associated with individual generators and they contribute significantly to the costs of wind turbines. Between
variable speed wind turbine generators, Doubly Fed Induction Generators (DFIGs) and Permanent Magnet
Synchronous Generators (PMSGs) with primary converters are emerging as the preferred technologies [2].
Doubly Fed Induction Generator (DFIG) is one of the most popular wind turbines which include an
induction generator with slip ring, a partial scale power electronic converter and a common DC-link
capacitor. Power electronic converter which encompasses a back to back AC-DC-AC voltage source
converter has two main parts; Grid Side Converter (GSC) that rectifies grid voltage and Rotor Side Converter
(RSC) which feeds rotor circuit. Power converter only processes slip power therefore it’s designed in partial
scale and just about 30% of generator rated power [3] which makes it attractive from economical point of
view.

 ISSN: 2088-8694
IJPEDS Vol. 4, No. 4, December 2014 : 419 – 429
420
Many different structure and control algorithm can be used for control of power converter. In this
paper, decouple PI control of output active and reactive power to improve dynamic behavior of wind turbine
which is one of the most common control techniques is presented. But due to uncertainty about the exact
model and behavior of some parameters such as wind, wind turbine, etc and also parameters values
differences during operation because of temperature, events or unpredictable wind speed, tuning of PI
parameters is one of the main problems in this control method. Based on the analysis, fuzzy logic controller
has been designed to improve the dynamic performance of DFIG.
In fuzzy logic control there is no need of a detailed mathematical model of the system and just using
the knowledge of the total operation and behavior of system is enough in designing the controller. The
performance of PI control is compared with that of fuzzy logic controller and it is investigated that the
dynamic performance of fuzzy logic controller is quite good in comparison with PI controller.
In this paper, the dynamic performance of DFIG under different fault conditions is investigated.
2. THE SAMPLE TEST SYSTEM
Sample test system is shown in Figure 1. It consists of three main feeders, two DG units and five
local loads. The two DG units are a DFIG and a synchronous generator. In the proposed system, different
cases of abnormal conditions are considered, when there is a single phase line to ground fault near DFIG, a
single phase line to ground fault on the grid and three phase line to ground fault near DFIG etc. The
configurations and parameters of the DFIG and synchronous generator system are extracted from [4]. Main
grid is represented by a three phase 69 kV voltage source with 1000MVA short circuit capacity and X/R ratio
of 22.2.
Connection point of main and micro-grid systems is called Point of Common Coupling (PCC).
2MVA DFIG wind turbine consists of power electronic converter control unit which feeds generator’s rotor
and grid. Power electronic converter unit is to control active and reactive power of generator separately and
to improve power quality and stability of the network. The parameters of 5MVA synchronous generator are
given in Table 1.
Figure 1. Sample test system
3. MODELING OF BASIC COMPONENTS
3.1. Wind and Wind Turbine
Wind effect plays a fundamental rule in wind turbine modeling especially for interaction analysis
between wind turbines and the power system to which they are connected. Wind model describes wind
fluctuation in wind speed which causes power fluctuation in generator. For wind model four components can
be considered, as describe in (1) [5]:
wind bw gw rw nwV V V V V    (1)
Where, Vbw = Base wind component (m/s); Vgw = Gust wind component (m/s); Vrw = Ramp wind component
(m/s); Vnw = Noise wind component (m/s).

IJPEDS ISSN: 2088-8694 
A Fuzzy Logic Control Strategy for Doubly Fed Induction Generator for Improved… (G. Venu Madhav)
421
The base component is a constant speed; wind gust component may be expressed as a sine or cosine
wave function or their combination [6]; a simple ramp function will be used for ramp component and a
triangle wave for noise function which it’s frequency and amplitude will be accordingly adjusted. The simple
block diagram for generation of wind speed is illustrated in Figure 2 and which includes all of four
components mentioned above.
For electrical analysis, a simplified aerodynamic model of wind turbine is normally used.
Accordingly wind blade torque from wind speed will be produced which is as follows:
rot
wind
R
V

  (2)
 2 3
w p wind
1
P R C , V
2
    (3)
 2 3
p windw
w
rot
R C , VP
T
2
  
 
 
(4)
Where Tw is an aerodynamic torque extracted from the wind (Nm), is the air density (kg/m3
), R is
the wind turbine rotor radius (m), Vwind is the equivalent wind speed (m/s),  is the pitch angle of the rotor
(deg), is the tip speed ratio, rot is the mechanical speed of the generator (rad/s) and Cp is the power
coefficient.
Cp can be expressed as a function of the Tip Speed Ratio (TSR) and pitch angle which is given by
(5) [7], [8]:
  i
12.5
p
i
116
C , 0.22 0.4 5 e

 
     
 
i
3
1
1 0.035
0.08 1
 
 
 
     
(5)
By increasing pitch angle, power coefficient and therefore torque decreases moreover Cp growth rate
changes in different speed by .
3.2. DFIG Model
As illustrated in Figure 3, DFIG system is a wound rotor induction generator with slip ring, with
stator directly connected to the grid and with rotor interfaced through a back to back partial scale power
converter. The converter consists of two conventional voltage source converters that are called Rotor Side
Converter (RSC) and Grid Side Converter (GSC) and a common DC-link [3]. Consequently the DFIG can be
regarded as a traditional induction machine with a nonzero rotor voltage.
Using the Concordia and Park transformation allows to write a dynamic model in a d-q reference
frame from the traditional a-b-c frame as follows [9]:
Electromagnetic torque:
 em ds qs qs ds
3
T i i
2
    (6)
Active and reactive power of stator:
 s ds ds qs qs
3
P V i V i
2
  (7)
 s ds ds qs qs
3
Q V i V i
2
  (8)

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Table 1. Synchronous generator parameters
Rated Power 5 MVA Rated Voltage 13.8 kV
Ra 0.0052 p.u Xls 0.2 p.u
Xd 2.86 p.u Xq 2.0 p.u
Xd 0.7 p.u Xq 0.85 p.u
Xd 0.22 p.u Xq 0.2 p.u
Tdo 3.4 s Tqo 0.05 s
Tdo 0.01 s H 2.9 s
Table 2. Induction generator parameters of wind turbine (DFIG)
Rated Power 2 MVA
Rated Voltage 0.69 kV
Stator/rotor ratio 0.4333
Angular moment of inertia (J=2H) 1.8293 p.u
Mechanical damping 0.02 p.u
Stator resistance 0.0183 p.u
Rotor resistance 0.0205 p.u
Stator leakage inductance 0.2621 p.u
Rotor leakage inductance 0.3152 p.u
Mutual inductance 5.572 p.u
Figure 2. Model of wind speed Figure 3. Schematic representation of a DFIG wind
turbine
Table 2 shows the parameters of the DFIG which is used in this proposed system. The rotor side
converter operates at the slip frequency. The power converter processes only the slip power, thus if the DFIG
to be varied within about ±30% slip, the rating of power converter is only about 30% of rated power of the
wind turbine [10].
Setting the stator flux vector to align with d-axis and assuming the per phase stator resistance
negligible, we have:
s ds s qs,V V    (9)
 s s s s sV r i dt    (10)
Substitution (9) in (7) and (8), the active and reactive power of stator flow into the grid can be
expressed as:
m
s s qr
m s
L3
P V i
2 L L
 

(11)
s s
s m dr
m s s
V V3
Q L i
2 L L
 
  
  
(12)

423
Where, iqr and idr are rotor current (A) in d- and q-axis respectively, Lls and Lm are stator leakage and
mutual inductance (H), s is the electrical angular velocity (rad/s) and Vs is the magnitude of the stator phase
voltage (V). This means that using vector control with d-axis oriented stator flux vector in rotor side
converter, active and reactive power can be controlled separately. This will be achieved by regulating iqr and
idr respectively.
Grid side converter is presented for keeping DC link voltage of capacitor constant regardless to the
magnitude and direction of rotor power. Neglecting power losses in the converter, capacitor current can be
described as follow:
dc
dc gcd dcr
dV 3
i C mi i
dt 4
   (13)
Where igcd stands for the d-axis current flowing between grid and grid side converter (A), idcr is the
rotor side DC current (A), C is the DC-link capacitance (F) and m is the PWM modulation index of the grid
side converter.
The reactive power flow into the grid from GSC can be expressed as:
g g gcq
3
Q V i
2
 (14)
Where Vg is the magnitude of grid phase voltage (V) and igcq is q-axis current of grid side converter
(A). Therefore it is seen from (13) and (14), by adjusting igcd and igcq, DC-link voltage and Qg can be
controlled respectively.
3.3. Pitch Control
To produce a maximum energy, the blade angle must be tuned with wind straightforward using pitch
angle control of wind turbine blades. It is worth noticing that we can use this characteristic in abnormal
conditions such as grid faults to protect generator from over speeding. In two different cases, an increasing
rotor speed may be occurred; a wind speed as input power and an abnormal case due to a fault existence.
These must be distinguished first, before a control takes place. When the output terminal voltage
falls under 0.9 p.u and the rotor speed is increased, it means a fault is happened.
To actuate the event and to decrease the rotor speed, the pitch angle must be manipulated. An
emergency pitch angle should be added with rate of +10(deg/s/1000rpm) for over speed protection.
4. A FUZZY LOGIC AND PI CONTROL STRATEGY
The four main components of fuzzy logic controller are fuzzification, fuzzy inference engine, rule
base and defuzzification. Inputs are fuzzified, then based on rule base and inference system, outputs are
produced and finally the fuzzy outputs are defuzzified and applied to the main control system. Error of inputs
from their references and error deviations in any time interval are chosen as inputs. Mamdani type fuzzy
logic control is considered here.
Figure 4. Rotor side converter fuzzy controller unit structure
Figure 4 shows the block diagram of rotor side converter with fuzzy controllers. Similarly, PI
controllers are used in place of fuzzy controllers. The main objectives of this part are active power control
and voltage regulation of DFIG wind turbine using output reactive power control. As illustrated in Figure 6

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rotor side converter manages to follow reference active (Pref) power and voltage (Vref) separately using fuzzy
controllers, hysteresis current controller converter and vector control algorithm. Based on (11), (12) and
Figure 6, inputs of fuzzy controller are error in active and reactive power or voltage and the rate of changes
in errors in any time interval. After the production of reference d- and q-axis rotor currents, they converted to
a-b-c reference frame using flux angle, rotor angle and finally slip angle calculation and Concordia and Park
transformation matrix. Then they applied to a hysteresis current controller to be compared with actual
currents and produce switching time intervals of converter.
Figure 5. Input and output membership functions of voltage controller
Figure 6. Input and output membership functions of active power controller
Table 3. Rule bases of voltage fuzzy controller
∆E (V)
∆Idr NB N ZE P PB
E (V) NB NB NB N N ZE
N NB N N ZE P
ZE N N ZE P P
P N ZE P P PB
PB ZE P P PB PB
Table 4. Rule bases of active power fuzzy controller
∆E (P)
∆Iqr NB N ZE P PB
E (P) NB NB NB N N ZE
N NB N N ZE P
ZE N N ZE P P
P N ZE P P PB
PB ZE P P PB PB
Figure 5 and 6 shows inputs and output membership functions. To avoid miscalculations due to
fluctuations in wind speed and the effects of noise on data, trapezoidal membership functions are chosen to
-0.3 -0.2 -0.1 0 0.1 0.2 0.3
0
0.2
0.4
0.6
0.8
1
delta-v
Degreeofmembership
NB N PBZE P
-0.01 -0.005 0 0.005 0.01
0
0.2
0.4
0.6
0.8
1
Ddelta-v
Degreeofmembership
NB N ZE PBP
-0.1 -0.05 0 0.05
0
0.2
0.4
0.6
0.8
1
Idr
Degreeofmembership
NB N ZE PBP
-1.5 -1 -0.5 0 0.5 1 1.5
0
0.2
0.4
0.6
0.8
1
delta-p
Degreeofmembership
NB N ZE P PB
-0.01 -0.005 0 0.005 0.01
0
0.2
0.4
0.6
0.8
1
Ddelta-p
Degreeofmembership
NB N ZE P PB
-0.01 -0.005 0 0.005 0.01
0
0.2
0.4
0.6
0.8
1
Iqr
Degreeofmembership
NB N ZE PBP

425
have smooth and constant region in the main points. Rule bases are shown in Table 3 and 4. NB, N, ZE, P
and PB represents negative big, negative, zero, positive and positive big respectively. For instance when E
(P), the error of active power and E (P), the rate of change of active power error in a time interval, are NB
mean the output voltage is more than reference and is increasing dramatically therefore reference q-axis rotor
current which controls active power should decrease rapidly that represents NB.
In this paper, Proportional and Integral (PI) controllers are used in place of fuzzy controllers as
shown in Figure 4 and the results of both the controllers are compared. PI controller blocks operate in the
feed forward path of both active power (P) and reactive power (Q) feedback loops. PI controller gains are
tuned by using the Simulink Control Design software which makes the control systems design and analyze in
Simulink environment.
5. RESULTS AND DISCUSSION
For investigation of dynamic behavior of proposed system with fuzzy logic and PI controller,
different situations and events are considered. Based on different fault locations and severity, the system has
different responses. In each condition, different parameters such as voltage, active and reactive power, rotor
currents and dc link voltage are taken to prove the capability of the proposed controller.
(a) Single line to ground fault near synchronous generator:
A single line to ground short circuit fault with duration of 0.1s is occurred near the synchronous
generator. The fault duration is from 5s to 5.1s. Figure 7 shows different responses of the synchronous
generator and DFIG in test systems. During the fault, there is little variation in active and reactive power of
wind turbine and in AC and DC-link voltages because the fault is far from the wind turbine and near the
synchronous generator, so, variation in active and reactive power of synchronous generator is high.
(a) (b)
(c) (d)
(e) (f)
Figure 7. Single line to ground fault near synchronous generator at 5s with duration of 0.1s (a) output voltage
(b) active power of synchronous generator (c) reactive power of synchronous generator (d) active power of
DFIG (e) reactive power of DFIG (f) dc-link voltage
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vrms(p.u)
Output Voltage
PI Controller
Fuzzy Controller
0 2 4 6 8
-4
-2
0
2
4
6
8
10
12
Active Power of Synchronous Generator
Time (Secs)
P(MW)
0 2 4 6 8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Reactive Power of Synchronous Generator
Time (Secs)
Q(MW)
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Time (secs)
P(MW)
Active Power of DFIG
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (secs)
Q(MW)
Reactive Power of DFIG
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vdc(p.u)
DC-Link Voltage
PI Controller
Fuzzy Controller

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GSC generally controls the dc bus voltage of the back-to-back converter and the exchange of
reactive power to the grid. The proposed controllers produce the necessary values of direct and quadrature
axis rotor currents which are converted into three phase currents to maintain control on the machine stability.
The power delivered from RSC will be increased due to increase of rotor currents and voltages which in turn
increase the dc bus voltage.
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 8. Single line to ground fault near DFIG wind Turbine (a) output voltage (b) active power of DFIG (c)
reactive power of DFIG (d) active power of synchronous generator (e) reactive power of synchronous
generator (f) dc-link voltage (g) d-axis rotor current (h) q-axis rotor current
(b) Three line to ground fault near DFIG:
To prove performance of fuzzy logic controller in comparison with decouple PI control and to
investigate dynamic behavior of doubly fed induction generator in one of the worst case situations, a severe
three line to ground short circuit fault is considered near the wind turbine. Figure 9 shows the waveforms,
there is reduction in voltage and it reduces to near zero. In addition, active and reactive deviations in DFIG
are the most severe. Rotor current reaches to its limit and crowbar protection unit short circuits the rotor and
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vrms(p.u)
Output Voltage
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.5
1
1.5
2
Time (secs)
P(MW)
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (secs)
Q(MW)
PI Controller
Fuzzy Controller
0 2 4 6 8
-4
-2
0
2
4
6
8
10
12
Time (Secs)
P(MW)
0 2 4 6 8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time(Secs)
Q(MW)
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vdc(p.u)
DC-Link Voltage
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Time (secs)
Idr(p.u)
d-axis rotor current
PI Controller
Fuzzy Controller
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Time (secs)
Iqr(p.u)
q-axis rotor current
PI Controller
Fuzzy Controller

427
rotor side converter but still stator is connected to the network and due to super synchronous operation of
wind turbine it can produce active power. The proposed controller maintains the rotor currents under their
safety limits without high over currents. Due to mitigation of the over currents of the rotor the back-to-back
converter is less affected by this perturbation which produces short dc bus voltage oscillations.
(a) (b)
(c) (d)
(e) (f)
(g) (h)
(i) (j)
Figure 9. Three line to ground short circuit fault near DFIG wind turbine (a) output voltage (b) active power
of DFIG (c) reactive power of DFIG (d) active power of synchronous generator (e) reactive power of
synchronous generator (f) dc-link voltage (g) d-axis rotor current (h) q-axis rotor current (i) voltage across
the crowbar resistance (j) pitch angle
0 1 2 3 4 5 6 7 8
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vrms(p.u)
Output Voltage
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
P(MW)
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time (secs)
Q(MW)
PI Controller
Fuzzy Controller
0 2 4 6 8
-4
-2
0
2
4
6
8
10
12
Time (Secs)
P(MW)
0 2 4 6 8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time (Secs)
Q(MW)
0 1 2 3 4 5 6 7 8
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (secs)
Vdc(p.u)
DC-Link Voltage
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Time (secs)
Idr(p.u)
d-axis rotor current
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Time (secs)
Iqr(p.u)
q-axis rotor current
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
-2
0
2
4
6
8
10
x 10
-4
Time (secs)
VoltageacrosstheCrowbarResistance
Crowbar Protection Switch Condition
PI Controller
Fuzzy Controller
0 1 2 3 4 5 6 7 8
0
1
2
3
4
5
6
7
Time (secs)
Theta(deg)
Pitch angle
PI Controller
Fuzzy Controller

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Furthermore, beside electrical protection, an emergency pitch angle is introduced with slope of ±10
(deg/s). When voltage drops under 0.8 p.u and wind speed is constant, emergency pitch angle due to external
fault activates to protect DFIG from over speeding and keep output power below rated value. As soon as
voltage and speed come back to normal situation it starts to decrease and returns to normal situation. Grid
side converter acts as STATCOM and tries to restore voltage. After rotor current returns under the limit and a
constant time delay, crowbar switch opens and rotor side converter continues to operate. As illustrated in
Figure 9, fuzzy control unit of wind turbine maintains good stability and restores parameters to their
predefined values as well in comparison with PI controller.
6. CONCLUSION
In this paper, dynamic performance of DFIG under different fault conditions with PI controller and
fuzzy logic control has been investigated. The PI controller and fuzzy logic controller has been designed and
implemented in MATLab/Simulink. To prove the performance of controller unit, the abnormal situations of
single line to ground fault near and away from DFIG and three phase line to ground fault near DFIG are
exerted on proposed system. The output voltage, active and reactive powers, dc-link voltage, direct and
quadrature axis totor currents are improved for fuzzy logic controller compared to PI controller for different
cases of fault near and away from DFIG. The performance of fuzzy logic controller is found quite
satisfactory in improving stability and power quality of wind turbine compared to PI controller. Closer fault
location to the wind turbine causes more severe effect and a three line to ground short circuit fault near the
wind turbine as the worst case in which voltage decreases until zero and rotor current exceeds its limit.
REFERENCES
[1] Datta R, Ranganathan VT. Variable-Speed Wind Power Generation Using Doubly Fed Wound Rotor Induction
Machine - A comparison With Alternative Schemes. IEEE Transactions on Energy Conversion. 2002; 17(3): 414–
421.
[2] Li G, M Yin, M Zhou, C Zhao. Decoupling control for multi terminal VSCHVDC based wind farm
interconnection. IEEE. Power Engineering Society General Meeting. 2007: 1-6.
[3] Holdsworth L, XG Wu, JB Ekanayake and N Jenkins. Comparison of fixed speed and doubly-fed induction wind
turbines during power system disturbances. IEE Proc. Gener. Transm. Distrib., 2003; 150 (3): 343-352.
[4] Katiraei F, MR Iravani and PW Lehn. Micro-Grid Autonomous Operation During and Subsequent to Islanding
Process. IEEE Trans. Power Delivery. 2005: 20(1).
[5] Youjie Ma, Haishan Yang, Xuesong Zhou, Li Ji , 2009. The dynamic modeling of wind farms considering wake
effects and its optimal distribution. World Non-Grid-Connected Wind Power and Energy Conference, 2009.
WNWEC 2009; 22 (2): 1 - 4.
[6] Reynolds MG. Stability of wind turbine generators to wind gusts. Purdue University Report TR-EE 79-20.
[7] Heier S. Grid integration of wind energy Conversion systems. Chichester: John Wiley and Sans Ltd. 1998; 35-302.
[8] Slaotweg G, H Polindcr, WL Kling. Dynamic modeling of a wind turbine with direct drive synchronous generator
and back to back voltage source converter and its control. Proceedings of the European Wind Energy Conference,
Copenhagen, Denmark. 2001; 1014-1017.
[9] Bose BK. 1986. Power electronics and AC drives. New Jersey: Prentice-Hall. 1986; 46-52.
[10] Jang J, Y Kim, D Lee. Active and reactive power control of DFIG for wind energy conversion under unbalanced
grid voltage. Proc. IEEE Power Electronics and Motion Control Conference. 2006; 3.
BIOGRAPHIES OF AUTHORS
G. Venu Madhav received his B.Tech. degree in Electrical and Electronics Engineering from
Jawaharlal Nehru Technological University, Hyderabad in 2002. M.Tech. degree in Power and
Industrial Drives from Jawaharlal Nehru Technological University, Anantapur in 2005. He is
pursuing Ph.D. from Jawaharlal Nehru Technological University, Hyderabad. Currently he is
working as a Associate Professor, Dept. of EEE, BVRIT, Narsapur, Medak Dist. He has
published several National and International Journals and Conferences. His area of interest is
Advanced Control strategies of Electric Drives, Microprocessors and Microcontrollers, Fuzzy
logic & ANN applications, and Network Analysis. Have professional society memberships in
IETE (M), ISTE (LM), IE (AM), SESI (LM) and IAENG (M).

429
Dr. Y.P.Obulesu received his B.E. degree in Electrical and Electronics Engineering from Andhra
University, Visakhapatnam in 1996. M.Tech. degree in Power Electronics and Drives from IIT,
Kharagpur, in 1998. He received his Ph.D. degree from Jawaharlal Nehru Technological
University, Hyderabad in 2006. Currently he is working as a Professor and Dean of R & D,
Dept. of EEE, LBRCEC, Mylavaram, Krishna Dist. He has published several National and
International Journals and Conferences. His area of interest is Advanced Control strategies of
Electric Drives, SMPS, Multilevel inverters, Harmonic minimization, power quality, FACTs,
Solar powered Electric Vehicles, DSP & FPGA control of power electronics and drives,
Wavelets, Fuzzy logic & ANN applications to power electronics and drives. Have professional
society memberships in IEEE, ISTE (LM) and SSI (LM).

A Fuzzy Logic Control Strategy for Doubly Fed Induction Generator for Improved Performance under Faulty Operating Conditions

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