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Controller Design of DFIG Based Wind Turbine by 
Using Evolutionary Soft Computational Techniques 

 

Om Prakash Bharti 
Dpt of Electrical Engineering 
Indian Institute of Technology 

Banaras Hindu University 
Varanasi-221005, (U.P), India 

opiitbhu@gmail.com  

R. K. Saket 
Dpt of Electrical Engineering 
Indian Institute of Technology 

Banaras Hindu University 
Varanasi-221005, (U.P), India 

rksaket.eee@iitbhu.ac.in 

S. K. Nagar 
Dpt of Electrical Engineering 
Indian Institute of Technology 

Banaras Hindu University 
Varanasi-221005, (U.P), India 

sknagar.eee@iitbhu.ac.in 
 

 
Abstract—This manuscript illustrates the controller design for a 
doubly fed induction generator based variable speed wind 
turbine by using a bioinspired scheme. This methodology is based 
on exploiting two proficient swarm intelligence based 
evolutionary soft computational procedures. The particle swarm 
optimization (PSO) and bacterial foraging optimization (BFO) 
techniques are employed to design the controller intended for 
small damping plant of the DFIG. Wind energy overview and 
DFIG operating principle along with the equivalent circuit model 
is adequately discussed in this paper. The controller design for 
DFIG based WECS using PSO and BFO are described 
comparatively in detail. The responses of the DFIG system 
regarding terminal voltage, current, active-reactive power, and 
DC-Link voltage have slightly improved with the evolutionary 
soft computational procedure. Lastly, the obtained output is 
equated with a standard technique for performance improvement 
of DFIG based wind energy conversion system.  

Keywords-DFIG; Wind turbine; PID controller; Matlab; 
Simulink; model; PSO; BFO; Fitness; function 

Abbreviations:   
DFIG: Doubly Fed Induction Generator 
PID: Proportional Integral Derivative 
PSO: Particle Swarm Optimization 
BFO: Bacterial Foraging Optimization  
WECS: Wind Energy Conversion Systems 
WRSG: Wound rotor synchronous generator 
PMSG: Permanent magnet synchronous generator 
SCIG: Singly excited induction generator. 

I. INTRODUCTION  
Wind power is considered probably the optimum available 

renewable energy sources and has widely developed in recent 
years due to its advantages such as low pollution, 
comparatively low capital cost involved and the short gestation 
period [1]. There have been several types of generators for 
wind energy conversion configuration. The older approach 
involved synchronous generators whereas the recent approach 
focuses more on induction generators of different types [2]. 
Simple induction generators have a few weaknesses such as 
reactive power utilization and unregulated voltage profile 
during variable rotor speed. These problems can be solved by 
employing power electronic converter or regulators. The DFIG 

is a wound rotor induction machine that can operate in super-
synchronous and sub-synchronous manner. The benefits of the 
DFIG as compared with fixed speed generators are that they 
improve power quality, reduce mechanical stress and 
fluctuations and exhibit excellent power imprisons [3]. The 
function of the DFIG associated with the grid is facilitated 
through a rotor as well as a network side converter. However, 
an inverter related to the rotor side is used to provide give a 
fundamental frequency to sustain stator frequency at an 
invariable stage, despite the variations in mechanical power. 
The control of DFIG presents a dual dilemma: to balance the 
velocity changes and reactive power.  In reality, DFIGs should 
be cut off from the network while the voltage inequity is more 
than 6% [3]. It has been described that the torque pulsation 
could be concentrated by using injected recompense current in 
the DFIG rotor. In [4], authors presented a relevant study of a 
simplified model, in which the authors compare the fifth plus 
third order model of DFIG followed using the investigation 
under faulted circumstances. However, in [5], authors offered 
magnitude along with frequency control of network associated 
DFIG based on a coordinated model for wind power production 
[5]. The numerical differentiation based additive model 
approximately the nominal working position of DFIG was 
used. The effectiveness of such type models can be validated 
from the outcomes offered. The ranking of wind turbines from 
800kW to 3 MW but wind farm assortment is 2MW to 200MW 
[7]. In this paper, we provide an alternate technique to design a 
controller for the DFIG system considered by [7, 8] using two 
bioinspired techniques. The obtained results are compared with 
existing solutions. The implementation of DFIG for voltage 
regulation at a remote location has been described in a previous 
work [21]. 

The global trend toward clean energy is an inspiration for 
more integration of wind-based electrical energy in power 
systems [9]. Vast and small wind turbines produce electrical 
energy for networks whereas they sustain stand-alone isolated 
areas like well [10]. However, the wind velocity changes 
radically depending on the environmental circumstances along 
with the time of operation. Therefore, there is a huge margin of 
speed difference. Such margins of speed alteration make the 
wound rotor induction machines appropriate for power 



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generation through wind energy [11]. Wind turbines can run 
either at fixed speed (actually within a speed range about 1 %) 
or at varying speed [12]. For fixed speed wind turbines, 
induction generator is instantly coupled to the grid. Hence, for 
a fixed-speed system, the turbulence of the wind will result in 
power fluctuations, which influence power quality. Power 
electronic apparatus are used to control the DFIG based 
variable-speed wind turbine. DFIG based wind turbine 
aerodynamics has thoroughly been described in [13-16, 17]. 

II. GENERAL IDEA AND WORKING PRINCIPLE  OF  DFIG 
A part of wound rotor induction generators, also known as 

DFIG, is one of the most frequently used scheme in the wind 
energy industries [18]. Currently, these types of generators are 
extensively accepted as one of the appropriate wind energy 
conversion systems. DFIG is in nature a wound rotor induction 
generator, and the rotor circuit is usually controlled by electric 
power devices to allow variable speed operation. DFIG stator 
winding associated directly to the grid by a power transformer 
and DFIG’s power is usually ranged from a few kilowatts to 
several megawatts. The size for a rotor converter is about 30% 
of a full capacity converter. During low wind speed, more 
electrical energy could be acquired from a variable speed 
DFIG, in distinguishing with a fixed speed wind generator [2]. 
The advantages of using DFIG in WECS is described in [2]. 
Whereas in [19] DFIGs have two operating modes, in (i) mode 
Nr>Ns, S is –ve, then generator in super-synchronous mode and 
both stators, as well as rotor windings, deliver power to the 
grid. while in (ii) mode Nr<Ns, s is +ve the generator in 
subsynchronous mode and stator winding delivers power to 
both the grid and the rotor winding. The mathematical 
modeling of DFIG is thoroughly explained in [20]. The 
machine data is available in per unit reactance form. A DFIG 
equivalent circuit can be seen in [21]. The sixth order relations 
for unbalanced operations, is as follows [7, 8]: 

0.000324 6 1.75 5 2366 4  7.9 6 3  7.5 9 2  5 12   2.18 14
6  2340 5  8.67 6 4  4.79 9 3  2.7 12 2  1.27 14  9.6 1

(
4

)
s s s e s e s e s e

s s e s e
G S

s e s e s e
     

    



 

III. PARTICLE SWARM OPTIMIZATION 
An overview and brief description for particle swarm 

optimization along with algorithm features is given in [22-28]. 
This approach is appropriate to solve nonlinear problems and is 
established on flock activities such as birds detect food using 
gathering. The first alternative of the PSO algorithm brings 
using having the inhabitants (named the swarm) for the 
applicant result (known as particles). These particles are turned 
in the search space using a few straightforward formulas. The 
motions of the particles are guided to their best-known position 
in the search space in addition to the entire swarm's most 
prestigious situation. Whenever an enhanced position is being 
detected, then it will move towards to guide the movement of 
the flock. The procedure is repetitive, furthermore using 
performing, so it is trusted, but not assured, that an adequate 
result will finally be detected. Here in this procedure, a set of 
particles is introduced in the d-dimensional search space using 
at random selecting velocity as well as position. The first 
situation of the particle is considered as the best place to 
initiate, as well as the velocity of the particle is updated based 

on experience to the other particles from the swarming 
population. 

A. A minute representation for the algorithm 
We set the values of PSO algorithm constants as inertia 

weight factor W = 0.3, along with acceleration constants C1, C2 
= 1.5. As we have to optimize three parameters that are; KP, 
KD, KI to the controller, and search optimal value in the three-
dimensional search space. So we arbitrarily formatted a swarm 
of “100” population in three-dimensional search space using 
[Xi,1 Xi,2 Xi,3] and [Vi1 Vi2 Vi3] as preliminary situation as well 
as velocity. Considered primary fitness function of each point 
along with the position with minimum fitness function is 
exhibited as gbest (initial value of global best Optima) as well as 
the optimal fitness function as fbest1 (Initial best fitness 
function). We ran the program by means of PSO algorithm 
through thousands (or yet additional numbers) of iterations; in 
addition to program brought back the final optimal value of 
fitness function as “fbest” along with last global optimum point 
as “Gbest.”  

B. The conception of fitness function to the controller design 
For best results we optimize all PID parameters ,of the and 

describe a three-dimensional search space in which all three 
dimensions present. Every precise point in search space 
accounts for a particular arrangement of [KP, KI, and KD]. For 
which an appropriate answer has found, and a fitness function 
determines the performance of the position to the PID 
parameters. This fitness function comprises numerous essential 
features to the performance index for design. Along with the 
best position in search space, the fitness function reaches an 
optimal value with four essential functions which defined 
fitness function. By this function; steady state errors, peak 
overshoot, rise time as well as settling time has been come out 
for the defined system. The contribution of these essential 
functions towards the innovative fitness function is determined 
by a scale factor. At best position fitness function has minimal 
value. The chosen fitness function is defined as follows: 

F = (1-EXP (-β)) (MP +ESS) + (EXP (-β)) (TS - Tr) 

Here F: Fitness function, MP: Peak Overshoot, TS: Settling 
Time, Tr: Rise Time, β: Scaling Factor (Depends on upon the 
alternative of the designer), scaling factor β = 1. In the Matlab 
library, we have to define a fitness function which has PID 
parameters as input values, as well as it returns fitness value of 
the PID based controller model as output and formatted as 
follows.  The Function [F] = fitness (KD KP KI) 

C. PSO based controller gain 
The PSO based controller gains for the 6th order transfer 

function reduced the model to the DFIG system, at best fitness 
function (fbest=0.1967) by several iterations which are given in 
Table I. 

TABLE I.  GAINS OF THE PSO-BASED CONTROLLER 

Gbest  Parameters KP KI KD 
Gains 39.9781 7.6902 0.0271 



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IV. BACTERIAL FORAGING OPTIMIZATION 

A. Description 
A description for bacterial foraging optimization with 

algorithmic features is given [29-33]. The algorithm is heavily 
based on the behavior of E.colli bacteria. In the bacterial 
foraging optimization process four mobile behaviors are 
mimicked: 

1) Chemotaxis: 
A chemotactic step can be defined as a tumble followed by 

a tumble or a tumble a run lifetime. To represent a tumble a 
unit length random direction, X (j), is generated; this will be 
used to describe the direction of movement after a tumble. In 
particular : 

Xi(j+1,k,l) =X i(j,k,l) + C(i)*X(j) , 

Where Xi(j,k,l) represents the ith bacterium at jth 
chemotactic, kth reproductive as well as lth elimination and 
dispersal step.C(i) is the size of the step taken in the random 
direction specified by a tumble( the run length unit). 

2) Swarming: 
E.Colli cells can work cooperatively self-organize into 

highly structured colonies with high environmental adaptability 
using a complex communication means. Overall, cells provide 
an attraction signal to each other, so they swarm together. The 
mathematical representation for swarming can be represented 
by:  

Jcc(θ,P(j,k,l)) =Ji cc(θ,θi(j,k,l)) =Σ[Dattract * EXP(-
Wattract *Σ( θm –θi m)2)]+Σ [Hrepellant * EXP (-Wrepellant 
*Σ (θm –θi m) 2)] 

Where Jcc(θ, P(j,k,l)) is the cost function value to be 
additional to the actual cost function, that is to be minimized to 
present a time-varying cost function. S is the total number of 
bacteria , P is the number of parameters to be optimized which 
are present in each bacterium and Dattract, Wattract,hrepellant, 
Wrepellant are different coefficients that should be 
appropriately chosen. 

3) Reproduction: 
The least healthier bacteria die with the other each healthier 

bacteria split into two novel bacteria each placed in the similar 
position. 

4) Elimination and Dispersal: 
It is probable that in the local surroundings, the lives of a 

population of bacteria changes either gradually (e.g., via 
utilization of nutrients) or abruptly due to some other influence. 
Events can occur that all the bacteria in a region are killed, or a 
group is dispersed into a new part of the environment. They 
have the effect of possibly destroying the chemotactic progress. 

B. BFO based controller gain 
The BFO algorithm process as a flowchart is given in [33]. 

The BFO based controller gains for the 6th order Transfer 
function reduced the model to the DFIG system, at the fitness 
function (function F = Fitness (KD KP KI)) by several iterations 
which are given in Table II. 

TABLE II.  GAINS OF THE BFO-BASED CONTROLLER 

V.  SIMULATION AND RESULTS 

A. Simulink response of the DFIG system 
Here Wind Farm - DFIG Average Model (MATLAB 13b) 

shows a 9 MW wind farm with the DFIG driven by a variable 
speed wind turbine. It consisted of six 1.5 MW wind turbines 
and linked toward a 25 kV distribution system exports power to 
a 120 kV grid through a 30 km, 25 kV feeder. In this 
illustration, the wind velocity is preserved invariable at 15 m/s. 
The response of the DFIG based wind turbine with the 
evolutionary soft computational procedure (PSO-based 
controller) regarding voltage along with current at the 
terminals, active power generated; reactive power requirements 
and DC capacitor voltage in Pu are shown in Figures 1 and 2 
respectively. AS shown, the response of the DFIG system 
regarding terminal voltage, current, active-reactive power and 
DC-Link voltage has slightly improved with the PSO based 
controller compared to the BFO based controller. 

 

 

 

 
Fig. 1.  (a) DFIG based wind turbine Average Matlab Simulink Model 
diagram, (b) Voltages at the DFIG terminals in Pu, (c) Currents at the DFIG 
terminals in Pu 

Parameters KP KI KD 
Gains 0.1417 0.1472 0.1005 

(a) 

(b) 

(c) 



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B. BFO based Control Response by PID Controller 
The reaction of the BFO based PID controller is given in [7, 

8]. As shown the system has zero undershot in the response of 
open loop system. The step response of the BFO based PID-
controller is shown in Figure 3 and Table III. 

C. Response of PID Controller Using PSO Method 
The step response of the PSO based system is given in 

Figure 4. It is observable by a comparison result of BFO based 
PID control along with one describe in this work which shows 
that; PSO based PID controller has extended to sufficiently 
damped BFO based controller without compromising the speed 
of response to the control loop. Now the step response of open 
as well as close loop reduced order system by PID controller 
with the PSO technique is shown in Figure 4 and Table IV. 

D. Comparison between BFO based PID controller and PID-
controller using PSO method 
By comparing Tables III and IV the following observations 

can be made:  

 The designed PSO based PID controller can reduce the 
steady state error to zero as the existing one. 

 The rise time, peak time and overshoot have been reduced 
to zero, and the response is no more underdamped. 

 The speed response is much better than that of the current 
controller. Hence intended controller in this paper assists to 
eliminate reactive power variations in DFIG system. 

 

 

 

 
Fig. 2.  (a) Active power delivered of the DFIG in Pu (b) Reactive power 
requirement of the DFIG in Pu (c) DC link voltage at the ordinary linkage 
capacitor 

 
Fig. 3.  Step response of the BFO based PID controller 

TABLE III.  STEP RESPONSE OF BFO BASED PID CONTROLLER 

Rise 
Time 

Settling 
Time 

Settling 
Min 

Settling 
Max 

Over 
Shoot 

Under 
Shoot 

Peak Peak 
Time 

0.00 
sec 

62.3541 
sec 

0.0438 
sec 

0.4992  
sec 

0.0 % 0.0 % 0.500 0.0  
sec 

 

 
Fig. 4.  Step response of the PSO-based PID controller 

TABLE IV.  STEP RESPONSE OF THE PSO-BASED PID CONTROLLER 

Rise 
Time 

Settling 
Time 

Settling 
Min 

Settling 
Max 

Over 
Shoot 

Under 
Shoot 

Peak Peak 
Time 

0 sec 0.4061 
sec 

-.0694 
sec 

0.4996 
sec 

0  % 13.8754% 0.50 0  
sec 

VI. CONCLUSION 
The  BFO based PID controller improves system responses 

in comparison with an open loop system, while the PSO based 
designed controller not only improves the system response but 
also reduces the percentage overshoot to zero. The obtained 
outcome demonstrates that the system using PSO based 
controller settles down in less time compared to the BFO based 
PID controller scheme. The comparison between the PSO 
based controller and the BFO based PID controller is discussed. 
It is accomplished that the settling time is condensed next to 
near about 62% along with the percentage overshoot; rise time 
and peak time are reduced to zero. Finally, it is fulfilled that 
PSO based control technique afford another alternative to 

(b) 

(c) 

(a) 



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propose a reliable and adequate controller to the DFIG based 
wind energy conversion systems. 

ACKNOWLEDGMENT 
 The authors are highly thankful to the Indian Institute of 

Technology, Banaras Hindu University, Varanasi, Uttar 
Pradesh, India for partial funding this work.  

Appendix: DFIG Simulation Data 
Base values:  Sb = 2 MVA, Vb = 690 V, ωb = 2Пf (rad/s), f = 60 Hz,  

Zdc = Vdc/Idc,   Ib = 1900A, Vdc = 1150 V, Zb = (Vb/ √ 3)/Ib,   Lb = Zb/ωb,  

Ldc = Zdc/ωb, , Cb = 1/ (Zb ωb), Tb = Sb/ωb, jb = Sb/ (ωb
2), Idc = Sb/Ydc,  

Cdc = 1/ (Zdc ωb) ,Infinite bus voltage (Pu): V dq, inf = [0.989   0.15] 

DFIG (Pu):  Rs = 0.023,   Rr = 0.016,   Ls = 0.180,   Lr = 0.16, 

 Lm = 2.9, DC link component: vdc
ref

  = 1,    Cdc = 12.7227  

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